{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifying cars with Decisions Trees, Random Forests, and Ensemble Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from sklearn.preprocessing import OrdinalEncoder, MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingClassifier, \\\n",
    "    VotingClassifier, BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz, ExtraTreeClassifier\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.metrics import mean_absolute_error, confusion_matrix, accuracy_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('C:/Users/Nicolas/Documents/Scraping/thecarconnection/full_dataset.csv', \n",
    "                 index_col=0).sample(frac=1, random_state=42).reset_index(drop=True)\n",
    "dataset = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking a peek at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSRP</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Passenger Capacity</th>\n",
       "      <th>Passenger Doors</th>\n",
       "      <th>Front Shoulder Room (in)</th>\n",
       "      <th>Front Head Room (in)</th>\n",
       "      <th>Second Leg Room (in)</th>\n",
       "      <th>Front Leg Room (in)</th>\n",
       "      <th>Second Shoulder Room (in)</th>\n",
       "      <th>...</th>\n",
       "      <th>Rear Wheel Size</th>\n",
       "      <th>Front Wheel Size</th>\n",
       "      <th>Tire Rating</th>\n",
       "      <th>Tire Width Ratio</th>\n",
       "      <th>Wheel Size Ratio</th>\n",
       "      <th>Tire Ratio</th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30034.0</td>\n",
       "      <td>GMC</td>\n",
       "      <td>Sierra 2500HD</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>65.2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>41.30</td>\n",
       "      <td>66.3</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19515.0</td>\n",
       "      <td>Ford</td>\n",
       "      <td>Ranger</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.5</td>\n",
       "      <td>39.2</td>\n",
       "      <td>39.1</td>\n",
       "      <td>42.40</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24595.0</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Outback</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56.3</td>\n",
       "      <td>40.8</td>\n",
       "      <td>37.8</td>\n",
       "      <td>43.00</td>\n",
       "      <td>56.1</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56600.0</td>\n",
       "      <td>Porsche</td>\n",
       "      <td>Cayenne</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>58.9</td>\n",
       "      <td>39.6</td>\n",
       "      <td>36.0</td>\n",
       "      <td>41.86</td>\n",
       "      <td>56.7</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SUV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21795.0</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>CX-5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>40.1</td>\n",
       "      <td>39.3</td>\n",
       "      <td>41.00</td>\n",
       "      <td>55.5</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SUV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSRP     Make          Model  Passenger Capacity  Passenger Doors  \\\n",
       "0  30034.0      GMC  Sierra 2500HD                 6.0              2.0   \n",
       "1  19515.0     Ford         Ranger                 5.0              2.0   \n",
       "2  24595.0   Subaru        Outback                 5.0              4.0   \n",
       "3  56600.0  Porsche        Cayenne                 5.0              4.0   \n",
       "4  21795.0    Mazda           CX-5                 5.0              4.0   \n",
       "\n",
       "   Front Shoulder Room (in)  Front Head Room (in)  Second Leg Room (in)  \\\n",
       "0                      65.2                  41.0                  33.7   \n",
       "1                      54.5                  39.2                  39.1   \n",
       "2                      56.3                  40.8                  37.8   \n",
       "3                      58.9                  39.6                  36.0   \n",
       "4                      57.5                  40.1                  39.3   \n",
       "\n",
       "   Front Leg Room (in)  Second Shoulder Room (in)  ...  Rear Wheel Size  \\\n",
       "0                41.30                       66.3  ...             16.0   \n",
       "1                42.40                       40.0  ...             15.0   \n",
       "2                43.00                       56.1  ...             17.0   \n",
       "3                41.86                       56.7  ...             18.0   \n",
       "4                41.00                       55.5  ...             17.0   \n",
       "\n",
       "   Front Wheel Size  Tire Rating  Tire Width Ratio  Wheel Size Ratio  \\\n",
       "0              16.0            R               1.0               1.0   \n",
       "1              15.0            R               1.0               1.0   \n",
       "2              17.0            R               1.0               1.0   \n",
       "3              18.0            V               1.0               1.0   \n",
       "4              17.0            H               1.0               1.0   \n",
       "\n",
       "   Tire Ratio    Year  Country  Country Code  Category  \n",
       "0         7.0  2002.0      USA           6.0    Pickup  \n",
       "1         7.0  2010.0      USA           6.0    Pickup  \n",
       "2         6.0  2010.0    Japan           2.0       Car  \n",
       "3         5.0  2014.0  Germany           0.0       SUV  \n",
       "4         6.0  2016.0    Japan           2.0       SUV  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Style Name', 'Body Style', 'EPA Classification', 'Drivetrain'], axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data contains 31237 rows and 81 columns, across 43 different makes.\n"
     ]
    }
   ],
   "source": [
    "print('The data contains %i rows and %i columns, across %i different makes.'%(data.shape[0], \n",
    "                                data.shape[1], len(data.Make.value_counts())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Making sure the data is balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None             10555\n",
       "Compact           4089\n",
       "Midsize           3585\n",
       "SUV 4WD           2150\n",
       "Subcompact        1762\n",
       "Large             1626\n",
       "SUV 2WD           1285\n",
       "Small SUV 4WD     1258\n",
       "Two Seater        1067\n",
       "Small SUV 2WD      881\n",
       "Name: EPA Classification, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['EPA Classification'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "suv_awd = data.loc[data['EPA Classification'] == 'SUV 4WD'].sample(n=1000)\n",
    "midsized_car = data.loc[data['EPA Classification'] == 'Midsize'].sample(n=1000)\n",
    "compact_car = data.loc[data['EPA Classification'] == 'Compact'].sample(n=1000)\n",
    "subcompact_car = data.loc[data['EPA Classification'] == 'Subcompact'].sample(n=1000)\n",
    "large_car = data.loc[data['EPA Classification'] == 'Large'].sample(n=1000)\n",
    "suv_2wd = data.loc[data['EPA Classification'] == 'SUV 2WD'].sample(n=1000)\n",
    "small_suv_4wd = data.loc[data['EPA Classification'] == 'Small SUV 4WD'].sample(n=1000)\n",
    "two_seater = data.loc[data['EPA Classification'] == 'Two Seater'].sample(n=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([suv_awd, compact_car, midsized_car, subcompact_car, large_car, suv_2wd, small_suv_4wd, two_seater], \n",
    "               axis=0, sort=False).sample(frac=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double checking the balanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Large            1000\n",
       "SUV 4WD          1000\n",
       "Small SUV 4WD    1000\n",
       "Two Seater       1000\n",
       "SUV 2WD          1000\n",
       "Subcompact       1000\n",
       "Midsize          1000\n",
       "Compact          1000\n",
       "Name: EPA Classification, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EPA Classification'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pie Chart of Car Types')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['EPA Classification'].value_counts().plot(kind='pie', cmap='summer')\n",
    "plt.title('Pie Chart of Car Types')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Separating the `x` and `y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, ['Drivetrain', 'Passenger Capacity', 'Passenger Doors', 'Body Style',\n",
    "       'EPA Classification', 'Front Shoulder Room (in)',\n",
    "       'Front Head Room (in)', 'Second Leg Room (in)', 'Front Leg Room (in)',\n",
    "       'Second Shoulder Room (in)', 'Second Head Room (in)',\n",
    "       'Height, Overall (in)', 'Wheelbase (in)', 'Width, Max w/o mirrors (in)',\n",
    "       'Fuel Tank Capacity, Approx (gal)', 'EPA Fuel Economy Est - Hwy (MPG)',\n",
    "       'EPA Fuel Economy Est - City (MPG)', 'Fuel System',\n",
    "       'Third Gear Ratio (:1)', 'First Gear Ratio (:1)',\n",
    "       'Fourth Gear Ratio (:1)', 'Second Gear Ratio (:1)',\n",
    "       'Front Brake Rotor Diam x Thickness (in)',\n",
    "       'Rear Brake Rotor Diam x Thickness (in)', 'Steering Type',\n",
    "       'Turning Diameter - Curb to Curb', 'Rear Wheel Material',\n",
    "       'Suspension Type - Front', 'Air Bag-Frontal-Driver',\n",
    "       'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear',\n",
    "       'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', 'Brakes-ABS',\n",
    "       'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control', 'Other Features', 'Basic Miles/km', 'Basic Years',\n",
    "       'Corrosion Miles/km', 'Corrosion Years', 'Drivetrain Miles/km',\n",
    "       'Drivetrain Years', 'Roadside Assistance Miles/km',\n",
    "       'Roadside Assistance Years', 'Hybrid Engine', 'Gears', 'Net Horsepower',\n",
    "       'Net Horsepower RPM', 'Net Torque', 'Net Torque RPM', 'Cylinders',\n",
    "       'Engine Configuration', 'Displacement (L)', 'Displacement (cc)',\n",
    "       'Rear Tire Width', 'Front Tire Width', 'Rear Wheel Size',\n",
    "       'Front Wheel Size', 'Tire Rating', 'Tire Width Ratio',\n",
    "       'Wheel Size Ratio', 'Tire Ratio', 'Year', 'Country',\n",
    "       'Category']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['EPA Classification']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28605          SUV 4WD\n",
       "12769       Two Seater\n",
       "25297    Small SUV 4WD\n",
       "3338           SUV 4WD\n",
       "5623             Large\n",
       "25878          SUV 4WD\n",
       "894            SUV 2WD\n",
       "8425             Large\n",
       "17162          SUV 2WD\n",
       "851            SUV 2WD\n",
       "Name: EPA Classification, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### One hot encoding certain variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "specs_to_dummies = ['Drivetrain', 'Fuel System', 'Steering Type', 'Rear Wheel Material',\n",
    "                   'Suspension Type - Front', 'Engine Configuration', 'Tire Rating', 'Country',\n",
    "                   'Category']\n",
    "\n",
    "for item in specs_to_dummies:\n",
    "    dummies = pd.get_dummies(X[item], prefix_sep=': ', prefix=item)\n",
    "    X = X.drop(item, axis=1)\n",
    "    X = pd.concat([X, dummies], sort=False, axis=1)\n",
    "X = X.reset_index(drop=True)\n",
    "\n",
    "specs_to_one_two = ['Air Bag-Frontal-Driver', 'Air Bag-Frontal-Passenger', 'Air Bag-Passenger Switch (On/Off)',\n",
    "       'Air Bag-Side Body-Front', 'Air Bag-Side Body-Rear', 'Air Bag-Side Head-Front', 'Air Bag-Side Head-Rear', \n",
    "       'Brakes-ABS', 'Child Safety Rear Door Locks', 'Daytime Running Lights',\n",
    "       'Traction Control', 'Night Vision', 'Rollover Protection Bars',\n",
    "       'Fog Lamps', 'Parking Aid', 'Tire Pressure Monitor', 'Back-Up Camera',\n",
    "       'Stability Control']\n",
    "\n",
    "for item in specs_to_one_two:\n",
    "    dummies = pd.get_dummies(X[item], prefix_sep=': ', prefix=item, drop_first=True)\n",
    "    X = X.drop(item, axis=1)\n",
    "    X = pd.concat([X, dummies], sort=False, axis=1)\n",
    "X = X.reset_index(drop=True)\n",
    "\n",
    "X.drop(['Body Style', 'EPA Classification', 'Other Features'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing the data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking what it looks like now..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Passenger Capacity</th>\n",
       "      <th>Passenger Doors</th>\n",
       "      <th>Front Shoulder Room (in)</th>\n",
       "      <th>Front Head Room (in)</th>\n",
       "      <th>Second Leg Room (in)</th>\n",
       "      <th>Front Leg Room (in)</th>\n",
       "      <th>Second Shoulder Room (in)</th>\n",
       "      <th>Second Head Room (in)</th>\n",
       "      <th>Height, Overall (in)</th>\n",
       "      <th>Wheelbase (in)</th>\n",
       "      <th>...</th>\n",
       "      <th>Child Safety Rear Door Locks: Yes</th>\n",
       "      <th>Daytime Running Lights: Yes</th>\n",
       "      <th>Traction Control: Yes</th>\n",
       "      <th>Night Vision: Yes</th>\n",
       "      <th>Rollover Protection Bars: Yes</th>\n",
       "      <th>Fog Lamps: Yes</th>\n",
       "      <th>Parking Aid: Yes</th>\n",
       "      <th>Tire Pressure Monitor: Yes</th>\n",
       "      <th>Back-Up Camera: Yes</th>\n",
       "      <th>Stability Control: Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>39.799999</td>\n",
       "      <td>33.700001</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>53.599998</td>\n",
       "      <td>39.799999</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>99.400002</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>36.299999</td>\n",
       "      <td>35.099998</td>\n",
       "      <td>44.299999</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>36.400002</td>\n",
       "      <td>46.099998</td>\n",
       "      <td>99.599998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>57.900002</td>\n",
       "      <td>39.299999</td>\n",
       "      <td>39.400002</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>58.099998</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>115.300003</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>40.900002</td>\n",
       "      <td>40.200001</td>\n",
       "      <td>41.200001</td>\n",
       "      <td>55.900002</td>\n",
       "      <td>40.099998</td>\n",
       "      <td>69.300003</td>\n",
       "      <td>112.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>59.299999</td>\n",
       "      <td>41.400002</td>\n",
       "      <td>41.799999</td>\n",
       "      <td>40.599998</td>\n",
       "      <td>57.700001</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>61.400002</td>\n",
       "      <td>120.699997</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 221 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Passenger Capacity  Passenger Doors  Front Shoulder Room (in)  \\\n",
       "0                 5.0              4.0                 53.500000   \n",
       "1                 2.0              2.0                 52.500000   \n",
       "2                 7.0              4.0                 57.900002   \n",
       "3                 5.0              4.0                 55.700001   \n",
       "4                 5.0              4.0                 59.299999   \n",
       "\n",
       "   Front Head Room (in)  Second Leg Room (in)  Front Leg Room (in)  \\\n",
       "0             39.799999             33.700001            43.599998   \n",
       "1             36.299999             35.099998            44.299999   \n",
       "2             39.299999             39.400002            41.000000   \n",
       "3             40.900002             40.200001            41.200001   \n",
       "4             41.400002             41.799999            40.599998   \n",
       "\n",
       "   Second Shoulder Room (in)  Second Head Room (in)  Height, Overall (in)  \\\n",
       "0                  53.599998              39.799999             65.000000   \n",
       "1                  53.000000              36.400002             46.099998   \n",
       "2                  58.099998              38.500000             69.000000   \n",
       "3                  55.900002              40.099998             69.300003   \n",
       "4                  57.700001              39.000000             61.400002   \n",
       "\n",
       "   Wheelbase (in)  ...  Child Safety Rear Door Locks: Yes  \\\n",
       "0       99.400002  ...                                1.0   \n",
       "1       99.599998  ...                                0.0   \n",
       "2      115.300003  ...                                1.0   \n",
       "3      112.500000  ...                                1.0   \n",
       "4      120.699997  ...                                1.0   \n",
       "\n",
       "   Daytime Running Lights: Yes  Traction Control: Yes  Night Vision: Yes  \\\n",
       "0                          1.0                    0.0                0.0   \n",
       "1                          0.0                    1.0                0.0   \n",
       "2                          1.0                    1.0                0.0   \n",
       "3                          1.0                    1.0                0.0   \n",
       "4                          1.0                    1.0                0.0   \n",
       "\n",
       "   Rollover Protection Bars: Yes  Fog Lamps: Yes  Parking Aid: Yes  \\\n",
       "0                            0.0             1.0               0.0   \n",
       "1                            0.0             0.0               0.0   \n",
       "2                            0.0             1.0               1.0   \n",
       "3                            0.0             0.0               0.0   \n",
       "4                            0.0             1.0               1.0   \n",
       "\n",
       "   Tire Pressure Monitor: Yes  Back-Up Camera: Yes  Stability Control: Yes  \n",
       "0                         0.0                  0.0                     0.0  \n",
       "1                         0.0                  0.0                     0.0  \n",
       "2                         1.0                  1.0                     1.0  \n",
       "3                         1.0                  0.0                     1.0  \n",
       "4                         1.0                  1.0                     1.0  \n",
       "\n",
       "[5 rows x 221 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Splitting the data into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=2e-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### #1 Classifier: Decision Tree "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf = DecisionTreeClassifier(max_depth=None, max_features=None, max_leaf_nodes=None, min_samples_leaf=1)\n",
    "tree_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = tree_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "trees_accuracy = accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With no parameter tuning, we have reached a 98.250000% accuracy with a decision tree\n"
     ]
    }
   ],
   "source": [
    "print('With no parameter tuning, we have reached a %f%% accuracy with a decision tree'%(trees_accuracy*1_00))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what were the most important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Height, Overall (in)</th>\n",
       "      <td>0.158507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passenger Capacity</th>\n",
       "      <td>0.144455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second Shoulder Room (in)</th>\n",
       "      <td>0.110121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Front Shoulder Room (in)</th>\n",
       "      <td>0.101879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering Type: Rack Pinion</th>\n",
       "      <td>0.099969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drivetrain: Front Wheel Drive</th>\n",
       "      <td>0.082920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drivetrain: Rear Wheel Drive</th>\n",
       "      <td>0.052212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Tank Capacity, Approx (gal)</th>\n",
       "      <td>0.048008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Importance\n",
       "Height, Overall (in)                0.158507\n",
       "Passenger Capacity                  0.144455\n",
       "Second Shoulder Room (in)           0.110121\n",
       "Front Shoulder Room (in)            0.101879\n",
       "Steering Type: Rack Pinion          0.099969\n",
       "Drivetrain: Front Wheel Drive       0.082920\n",
       "Drivetrain: Rear Wheel Drive        0.052212\n",
       "Fuel Tank Capacity, Approx (gal)    0.048008"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = pd.DataFrame(tree_clf.feature_importances_, index=[X.columns], \n",
    "             columns=['Importance']).sort_values(by='Importance', ascending=False).head(30)\n",
    "imp.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(imp, hist=False, kde_kws={'shade':True}, color='mediumvioletred')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Decision tree visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's nice with decision trees is that you can visualize them every step of the way. Let's make a very simple model so we can see the decision making"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file=r'C:\\Users\\Nicolas\\Anaconda3\\pkgs\\graphviz-2.38-hfd603c8_2\\Library\\bin/graphviz/cars_tree.dot',\n",
    "    feature_names=X_train.columns,\n",
    "    class_names=y.unique(),\n",
    "    rounded=True,\n",
    "    max_depth=3,\n",
    "    filled=True,\n",
    "    rotate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# type this into the command line: \n",
    "# dot -Tpng C:\\Users\\Nicolas\\Anaconda3\\pkgs\\graphviz-2.38-hfd603c8_2\\Library\\bin\\graphviz/cars_tree.dot -o \n",
    "# C:\\Users\\Nicolas\\Anaconda3\\pkgs\\graphviz-2.38-hfd603c8_2\\Library\\bin\\graphviz/cars_tree.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d7096d7390>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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CO9vrMNST7TpSkUu1gvVWt1yqN5dqhdyrNxclvxZAZvZ3mPexxx4DEv+eJ6nmmTFjBgAXXHABAH/6058AaNmyZdZqUm7YvHkzABdddFHstqeffhqAs88+u8rjm8gtSZIkSZIkSZIkSZIkSZIkSZIkSZIkSWlWL9sFSJIkSZIkSZIkSbXZq6++CsCECRMAmDVrFgDnnHMOANOmTQNg4MCBGa9NkiRJkiRJkpSaxdvWAjCl5+gsV1K+XKoVrLc6hVoht+rNhVoh9+qVpNouJCefdNJJAPz+978H4KabbspaTcoNYa2EtQPpSeIOTOSWJEmSJEmSJEmSJEmSJEmSJEmSJEmSpDQzkVuSJEmSJEmSJElKo2XLlgFw6623AvDss88CMHLkSADmzp0LQEFBQRaqkyRJkiRJkqTclv/KPYfdVjj8xmqfd+G2QgCu7Dy02ueqqlyqFay3OoVaIbfqzYVaIffqzSUlneslKVW/+tWvABgyZAgQT1bu169f1mpSzfTuu+8C8MILLwCwePHiapnHRG5JkiRJkiRJkiRJkiRJkiRJkiRJkiRJSjMTuSVJkiRJkiRJkqQq+PDDDwG44447AHj00UcBGDhwIADTp08H4r/yLUmSJEmSJEnKPbmUuJtLtYL1VqdcqhWsV5KUHsceeywA48aNA+BHP/oRAA888AAA7dq1y0pdqjnWr18PxNdGWCth7aSbidySJEmSJEmSJEmSJEmSJEmSJEmSJEmSlGYmckuSJEmSJEmSJEkVEH6Z+Sc/+QkAU6dOBaBr165APJH7oosuAuCoo/xtZUmSJEmSJEmqrMLhN2a7BElShnnul5QOkydPBuCVV14B4unL99xzDwCNGjXKSl3Knt27dwPxtdCtWzcgvlaqi/9qRJIkSZIkSZIkSZIkSZIkSZIkSZIkSZLSzERuSZIkSZIkSZIkqRTbtm0D4O67747ddu+99wKQl5cHwC9/+UsALrvsMgAaNGiQyRIlSZIkSZJ0hJg5cyYAY8aMAeDkk08G4glinTp1AiA/Px+Azp07J1wCdO3aFYh/tyVJkiRJtVVI3J4xYwYAw4YNA+CWW24B4M477wSgefPmWahOmbR161YAJk6cCMD27dsBePnll4HqT2c3kVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmS0sxEbkmSJEmSJEmSJKnI7t27AZg6dSoAP/3pTwHYv39/rM+tt94KwHXXXQdA06ZNM1miJEmSJEmSjlBr164FYPPmzQA899xzANSvXz+hX/gu6+DBg6WOFdLGQop3SO0++uijE25fuXJlWmqXypL/yj0J7cLhN2apEqXbtgN7APjnpg8AmL/1YwAeXfdmQr/ru5wIQEGLrkWX3codO13rJnmcN4ddDcCghVNjt13ccRAAk3ucXqk5kt2y6iUgcT+8dvwVUT0NK5YImryPn9mwHICZG6Pz9+jWPWN9R7fqAcCZrXsB0LZ+kzLrWn7CNQDk1W2YUg19FzxQap/KjlXWvk8+duUJY53UvAsAp7Y6OqWaJEm5r127dgA8//zzAJx99tkAfO973wPgZz/7GQBdunTJQnWqTh9/HL3/nDBhAgD16kX/pTqshbA2qpuJ3JIkSZIkSZIkSZIkSZIkSZIkSZIkSZKUZiZyS5IkSZIkSZIk6YgV0okeeeQRACZNmgTA+vXrAbj22msBuOWWW2LbtGrVKpMlSpIkSZIkSQCMGDEioX3o0CEA9u7dW+Gxdu/eDcAHH3yQcDl//nyg7DRvSSrPhn07Abhp5YtAPB26NPd9/GrCZUiRvr/3WUBmE5NDSnVICS9e13c7HQ9Aj8aV+zvBql2bgHjidUiIhsoncV/7XpQmWdo+Ln57uD5z0yoA7u55BhB/zAObtk/Ydv3e6DjmNS57/7+3c2O59a7ZvRWA/k3LTr0McwbJNVVF2O/hMqyz5P0gSaq9+vXrB8Brr70GwJe//GUArrrqKgAuu+wyAM4777zYNnXr1s1kiaqiAwcOAPDcc88B8NBDDwEwaFD0vuuvf/0rAG3bts1oXSZyS5IkSZIkSZIkSZIkSZIkSZIkSZIkSVKamcgtSZIkSZIkSZKkI0ZIKfrLX/4CwO233w7Ae++9B8R/YXvixIkAdO7cOdMlSpIkSZIkSSXq0aMHAO3aRWmm69evT/scRx2VmBN2xhlRSunf//73tM8lqfb68/plQDwBekrP0QCc2boXcHjycUjwfnjtEiCegP3PTR8AcH7bvtVc8eHOadMndj3UM3/rGqDyidxv7/gkoT2m3YBKVhffN8n7+Ly2xwDxFPOQ3A0wtXAREH88f24eHacrOw8FoHujlglzrN27DSj/8S7cVnjYbdP6Rgmnly6PUi+XbF8LlJ/IHeYMkmsqS+HwG8u8P6yzFza+D8D4lTMBuIkXE2qWJNV+IY35H//4BwB33HEHAPfeey8QT3MGuPrqqwEYNmwYAHXq1MlYnSpf+DcgIWX917/+NQAff/wxADfccAMAkyZNAqBhw4aZLhEwkVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmS0s7/yC1JkiRJkiRJkiRJkiRJkiRJkiRJkiRJaVYv2wVIkiRJkiRJkiRJ1e0f//gHALfeeisACxcuBGDMmDEAPPvsswD07t07C9VJkiRJkiRJqTvppJMAmDFjBgAHDhyo8ph169YFoFevXgD8NcPfRwAAIABJREFU4Q9/AOD9998H4O9//3uV55B05Lhz9b8S2mM7HFdm/7b1mwBwdf5QAO77+FUAxq2IznPnt+2b7hLL1b9pu9j10a17AjB+5UwAzmt7DAB5dRtWaMzweMJ4Q/I6Vbq+ZzYsT2iXto+L15i8f8NxurJzdHv3Ri0Ttn1nx6cAFLToVmYtYZzwuJKvQ3zflbcWwpxBck1VEdZZqKFwzzYgvj8Wb1sLVO24SJJyS8OG0evk5MmTAbj88ssBuOGGG2J9xo8fD0DXrl0BGDVqFACDBw8GoHv37gA0b94cgAYNGlRz1UeOvXv3ArB161YAVq9eHbtvyZIlAMyePRuANWvWAHDuuecC8OKLLwLxz7jZZiK3JEmSJEmSJEmSJEmSJEmSJEmSJEmSJKWZidySJEmSJEmSJEmqdRYtWgTAhAkTAHjppZcAOPPMMwF4/fXXgfivZEuSJEmSJEnZdujQIQDeffddAObMmQPA3LlzE9pr10aJoXXq1Kn0XPXr109oT5w4EYAf/OAHCfeHRG4p2bYDewD456YPgMR04JkbVwJwccdBAHy30/EA9GjcqkpzLtuxHoA5Wz6M3Zac/BxSgL9SlOBcXpLzvC0fAfC3z1YA8Oi6Nw/rc32XEwE4p00fIDGpuTJjJo+XypjlyX/lniptX1zh8BurPEY49uGxb9i3E4gnIpcmpEeno4Z0CusprO33dm4EUk9uDknPyeNVxbS+X67wNuUliOc3bJ7Qnr/1YyCe2J1s1a5NCe3RrXoc1mdKz9FAPJE7bFPa+SDMWVpN6XRBu/5APJF74bZCwERuSTqShfTm5557LnZb+Hw2bdo0AKZPnw7AY489ltnilKB//+h1/Bvf+AYAl156KQD9+vXLVkllMpFbkiRJkiRJkiRJkiRJkiRJkiRJkiRJktLMRG5JkiRJkiRJkiTlvOXLo7SX22+/HYA///nPAJx4YpRsMmvWLABOPvnkzBcnSZIkSZKkI96+ffsAWLx4MRBP2Z49e3aszyuvvALAhg0bAMjLywPi33F95zvfASA/Px+Ayy+/vMJ1HHVUlAM2dGiUrvo///M/ABxzzDEVHktHtmvfex6IJxSXJKQxh8uZg74FVDx9Osxx6fK/ptw3ua7kZO6KjBnSesPlkwMuBKCgRbdKjZk8Xllj5qpvdUhM5L5p5YsAjG0/EIivgepMW06n5OPy5PqlQOrJzaF/aeNlSnKC9oN9zi6xX6qJ6m/v+CShPbjZ4fsj+baVu6I08+RE7jBHeB6FGqpTcg3lJZBLko5MIeH55z//ecLl5s2bAVi6NHqd/+yzzwDYs2dPpkustRo2bAhAmzZtABgwYEDsvpYtW2alpsoykVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmS0sxEbkmSJEmSJEmSJOWUNWvWAHDnnXfGbps2bRoQTw565plnADj//PMzW5wkSZIkSZKOSDt27ABg/vz5QDxxe86cOQAsWLAgoV/79u0BKCgoiI1x2223ATBixAgAPv/5zwNQr17iP/k+ePAgAN///vcB2L59e4k11a9fH4AGDRrEbrv77rsBuPLKKwGoU6dOyo9RgsMTr6/vEiXGX50fT7DNqxsl5z27YTkA41bMAOCRT6J038k9Tq/QnMkJ138b+M3Y9eRE5MI9WwH4wuu/S5g7OZE7eczXjr8CKDkhevG2tQCc+/Yfo/k/WwEcnqqc6pjJ45U1ZqoKh99Yqe2qS0jcnjP4OwD8du3rQOlp5RO7nwzAsLx8IPWk60wJSdShzjtX/wuAa/O/AJSeLB7WY0i2Ds+X5GTrTHl6/TIARrfuCcCprY4usd/Apu0T2p/ujV67kusOCdZB10aH74fk2/6x+YOEGpLnKK2GTAjnNUmSUhESoYt/ppNKYyK3JEmSJEmSJEmSJEmSJEmSJEmSJEmSJKWZidySJEmSJEmSJEmq0TZs2ADA5MmTAXjwwQeBeGoRwEMPPQTAf/3XfwFw1FH+nrEkSZIkSZLSZ/369QDMmzcPgNmzZye0Fy9eDMD+/fsB6NGjBwAjR44E4JvfjBKEQ1pb376J6cAVEb77CmO9+OKLQDxdOyR2f+lLXwJg6tSpsW3z8/MrPa8E8TTd4DudBgPxFO7iQgp2chp2RSWnTW/YtzN2fdmO6LkZko+XbF+X0pghDTgk8E4vSsQ+tigF+LhmHWJ9Qzp0eanXqY6Z6ni1QY/GrYB4CvtNXYcD8NHuLQC8uzM6fjM3rQLiSdchuXp8t5qVcDmyxecS2ou2/QcoPZE73B+c06ZP9RRWhikfzYtdv+/jVwGYOehbQMnPW4B+TdoltMPzKySth+dgctJ4SeOF20KfUENYCyHlO8wRdG/UspxHJkmSlDv8FyySJEmSJEmSJEmSJEmSJEmSJEmSJEmSlGYmckuSJEmSJEmSJKlG2b59OwC/+MUvALjnnnsAaNgwSm0IydxXXXVVbJsGDRpkskRJkiRJkiTVIh98ECUMz507F4A5c+bE7gu3LV++HIinXh977LEAjBo1CoAbbrgBgBEjRgCZSb4++eSTAXjhhRcAaN26NQC/+c1vAPja175W5TmaNWuW0N5zMEocb3iU/wz9SBUSeIOQppsJIVk4JPpWxfiuUdJzSM8OSdBBSNcGuKLTEAAKWnRLy5ipjlcR+a/ck7axqjMpPKyXcBnSycd2OA6AeVs+AmDM0qcAyG+Yl3B/toVE6nAsx62YAZSeOh/uD/3D9plQ0vMlJHGXV0eHBk0T2qt2b0poh0T1YHCzjuXWU9Cia0I9YYywFpLnyEQi97YDexLaxZ/3OrKF9zvFJb8nkiSpIkzkliRJkiRJkiRJkiRJkiRJkiRJkiRJkqQ086fQJEmSJEmSJEmSlFV79+4F4klBP/nJTwDYtWsXADfeGKV/hFQjf/FekiRJkiRJqTh48CAAS5cuBeJJ2yFle/bs2QAUFhYC0LBhQwCGDRsWG+P8888H4K677gLiidstWrSo1tpTcckllwBw4MABAK655hoAWrZMX4ppmzZtEtqb9+8GoEMDv6NT5jz2yVtAPMn34o6DYved26YPAK3qNQagfVGS8KCFU8scMyQSh/TpZTvWAzBny4dAYpp2SNgOab0heTs51TjVMZPHK2vMXFJSKnhF072TU8rHr5wJlJzIHfZf2J8hXTmvbsOU5qpKGvPY9gMT5l68bS0QTxgP7eT+1WnDvp0APLx2CQDLdhatv8HfifXp0bhVSmPlN2ye0A5r98rOQwFYuK0w4f5U1u0xTdomtMMYYZ8lJ9gn11Ad1uzemtAe3apHtc+p3LCp6P1Oce3a5e75WZKUfSZyS5IkSZIkSZIkSZIkSZIkSZIkSZIkSVKamcgtSZIkSZIkSZKkjApJSI899hgAEydOBGDdunUAjBs3DoAJEyYA0LZt2+QhJEmSJEmSJPbu3QvAokWLgMMTt+fNmwfApk2bAGjePEr3LCiIkm+vvvpqAEaNGgXEk7gbNWpU7bWnQ+fOnQH44Q9/WG1z9O/fP6H9752fASZyH8lCGvaj694E4inAbes3qbY5QypzMLnH6aX2TU5ZTlVIFA6X5xQlfQOs3r0ZgDFLnwLiKczlpU2XNmbyeBUZszSV3S6dJnY/OXY9pCvP2/IRcHjSdmkqcvxOat4FiO+793ZuBOIJz+V5a/snJY6XihNbJPZ9adOqhLlDu7T+6RSS36esiV7z+jeJ1tvdPc8AqvbcvL7LiQDc9/GrABTuiRKsw/ENKeappGeHOsI2YYziz7Xic2bC9M9WJLQHN0tt7aj2W1H0fqe45PdEkiRVhInckiRJkiRJkiRJkiRJkiRJkiRJkiRJkpRmJnJLkiRJkiRJkiQpI5577jkAbrvtNgCWL18OwCWXXALEk7m7dUstmUOSJEmSJEm117Zt2wB45ZVXgHjK9uzZs2N9Fi5cCMCuXbsA6NixIwAjR44EYNKkSQntgQMHAlC3bt1qrb02adWqFQDH9u0HwIKtHwMwquXnslaTsiukFodE7ofXLgHg6vyhsT55dRsC8OyG6DvgcStmAPE077IStVOxatem2PUejaM1GpKcpxYuSmmMW1a9BMQfx98GfhOIJyqnkjCciTFzyehWPWPXQ9pySB1/csCFABzXrAMQXyNBOKZPr1+WcPuDfc4udb6RLRLPQ+e+/Ufg8P2ebPG2tQD8bu3iMscrS6g/pJAnp0uHBOtwf/LjTYeQjj36zUeAeJL1+G4FaZvjmCZtEtrJCdZfadu3wmOGbUKSevKYyXOm04Z9OwF4YeP7QPw4hXNT/6btqm1u5Zbwfgfi74FatGiRrXIkSbWAidySJEmSJEmSJEmSJEmSJEmSJEmSJEmSlGYmckuSJEmSJEmSJCntiicj3XLLLQC8+mqUbPDVr34VgCeffBKAfv36Zbg6SZIkSZIkZdu6deuAeNJ2uJwzZw4Ab74ZJdoeOHAAgD59opTTESNGxMb49re/nXBbr169qrvsI9aXL/gaAH9+4PcA3Ez6El+VW84vStN9pihtOyTahsuyfKvDoErNGVKZQ7L3yCUPV3iMkPgcErzHtBsAxNOzQ5pzKqb0HF3i7dUxZi4J+xYOP2YhmTtVIV36/DISn0N6cugb1mBF9nvxWiuTxpyc4h3SsUu7P51mbV6d0K7IczFZ4fAbS7y9a8PEBOKQPB70blzx9OyBTTuUOWbynBWR/8o9Feo/unWUIn9T1+GVnlO10wvbV8euX/DtK7JXiCSp1jCRW5IkSZIkSZIkSZIkSZIkSZIkSZIkSZLSzERuSZIkSZIkSZIkVdmSJUsAuO222wB4/vnnY/eddtppACxYsACAYcOGZbg6SZIkSZIkZdr7778PHJ60HdorVqwAoG7dugAMGhQl9Y4cORKACRMmAPG07Y4dO2aibJXi8ssvB+BnP/0pAK9v+w8Ax+d1zlpNyq77e58FwHMb/g3A+JUzD+sTkpIvaNcfSExsroiQyrz9wN6U59p9cD8QT0iev3VNQg1D8joBMHPQtwCY/ll0TiopzTiMPbhZdB4KKb7JUh0zebyyxsxV4ZgNLTpHhPTomZtWRZcbVyb0D/ukoEXXostuKc81vlsBAOe06QPAnC0fAocnPQch/fyk5tFclV2XEE/xDscvPK7QrkzKd6pKeh6kW4cGTcu8vzKPr7z9Xd6cVXFxx+i9xknNuwBwaqujAcir27Da5lRuCe9vVmz9NHbbZZddlq1yJEm1iInckiRJkiRJkiRJkiRJkiRJkiRJkiRJkpRmdQ4dOnQo20VIkiRJkiSpZvvPf6JfnJ0+fToAL82Mftn5jdcXA7Duk08A2LZzRxaqkyRJyi2NGkTJDq1atARgwMBjARh1ysmxPmedFaXZDB06NMPVSVLNtGjRIgCef/55AGbPihKVlr79TqzPpi2bAdi9d0+Gq5MkSdlwVJ14jk3L5s0B6HF0lKj3heEnAfClL30JgNGjo9TFRo0aZbJEqVY6cOAAAG+//TYQT9lOvgRYt24dAI0bNwZg2LBhAIwaNQqAgoKChMu8vLxqrV3pcdUV3wXgnWdfAuDJ3l/NZjmSJElpM+a9vwBw7Pmnx2779e9+m61yJEm1iInckiRJkiRJkiRJkiRJkiRJkiRJkiRJkpRm/kduSZIkSZIkSZIkSZIkSZIkSZIkSZIkSUqzOocOHTqU7SIkSZIkSZJUs7zxxhsA/GjiRACmz5gBQF6DRgAMb94VgIFN2gPQvkFTAJrVbZjROiVJUs1x4NBBAI6qE/2OcJ1sFlPD7Tm4H4BN+3cBsGLnZwC8sqMw1ueDrRsAOLZffwAm/PA2AL7xjW8AUKeOe1hS7RT+CcPjjz8OwM9+/BMA3nl3GQBH57UFYHiTzgD0btI6tm2reo0BaHRUvcwUK0mSsuoAB2PXt+7fA8Dq3ZsBWLLrUwBe3xx9zmqelwfAd6++CoAf/OAHALRs2TIzxUo5ZPfu3QAsXLgQgDlz5gAwd+5cAObNmwfA1q1bAWjVqhUAw4cPB2DkyJGxscL1oUOHAtCgQYNqrV2ZsX79egB69+gJwF35pwBwdps+2SpJkiSpSmZ8tgKAmwtnAfDeqpWx+9q1a5eNkiRJtYyJ3JIkSZIkSZIkSZIkSZIkSZIkSZIkSZKUZiZyS5IkSZIkiQ0bosTHOybeAcBvf/sbAI5r3gmAqztFSQlntOkFQP06/j6gJElSdXpn+ycAPPSfxQA89ck7AHxh6DAAHpj6IADHH398FqqTpPR7/fXXAbjm6nEAvLYoSv+7oP0AAC7rNBiAY5u2z0J1kiQpV23YtxOAJ4o+U/36k+gzVt0mjQCYfNcUAC699FIAjjrK775V+23ZsgWIJ2wnX4Yk7j17oqT7/Px8AEaNGgXAiBEjgHja9oAB0Xt2nz9Hnl/ddx8Ad064HYDp/b4eu69boxZZqUmSJKkiPtodvTc+590nAJj4s/8G4Lrrr89aTZKk2slvTSRJkiRJkiRJkiRJkiRJkiRJkiRJkiQpzUzkliRJkiRJOkK99tprsevnn30OAId2RukKt3QpAGBMx4EA1MlwbZIkSUoUEronrp4FwGub1gAw+eeTAbj55puzUpckVdVdd90FwC0/uAWAL7TsAsCkz50MmMAtSZLSa+v+6Dvwu9a8AsAj694E4NQvfhGAJ//8NAAtWpgkq9xUWFgIHJ6yPWfOnFift99+G4Dwz4f79u0LHJ60HdpHH310dZetHLV//34AzjjtdADWvfnv2H3P9L0AgLy6DTNfmCRJUjm2HYg+G35lefQZsOOgYwB48R8vAVCvXr3sFCZJqrVM5JYkSZIkSZIkSZIkSZIkSZIkSZIkSZKkNDORW5IkSZIk6Qjz1FNPAXDJxRfHbjulRXcA7ut9FgDN6jbIeF2SJEkqX/jD3rT/LAbgjlUvA3DppZcC8OCvpwJQv379TJcmSSnZt28fAOOuuhqAadOmATCp+ykAXNLp8wDUyXhlkiTpSLR0x6cAfOe9vwHQvEtHAKY//7+AScSqeZYvXw7AvHnzgHjSdrhctWoVEE8QHDJkCAAFBQWxMUaNGpVwW7t27aq7bNVyW7ZsAWDYkONjt7XfHKV1P9z7PMC/PUqSpJph+4G9AHznvecA+LRl9L554eLXAWjRokV2CpMk1XomckuSJEmSJEmSJEmSJEmSJEmSJEmSJElSmpnILUmSJEmSdIR47LHHALi4KIn76i5fiN1369FR+sJRZp5JkiTllH9tWg3AlSui9LjRZ30JgKf+8mcA6tTx/Z2kmiH804QLv/o1AGb+798B+HXv/wPAyS27Z6UuSZIkgE/37gDiydyfNjoIwKsLX4v16dKlS+YL0xFh//79setvvPEGAHPnzgVg9uzZQDyB+9NPoxT5pk2bAnDCCScAMGLECABGjhwJwEknnZTQT8qE999/P3Z9VEG0Jlvujtp/KErmzm/YPON1SZIkFe7ZCsAlRUncmxtFt8+eF73v7tWrV1bqkiQdOUzkliRJkiRJkiRJkiRJkiRJkiRJkiRJkqQ0M5FbkiRJkiSplluwYAEAp4yKUrf/v/wTAbi264lZq0mSJEnptWxHlMj11XeeAGDc968D4GeTJ2etJkkqbsIttwDw4C9/BcCf+10IQP+m7bJWkyRJUrLdB6Nk5LH//isAOzo2i90X0rmbNGmS+cKU03bt2gXE/14TUrZD6varr74a67tt2zYA2rRpA0BBQQEAo4r+xhOSt4cMGQJA/fr1q7V2qbIKCwsBOO/scwD4eMUqAO7pfjoAp7Y6OjuFSZKkI8o/N30AwI2rXwKgS58eADw3YzoA+fn52SlMknTEMZFbkiRJkiRJkiRJkiRJkiRJkiRJkiRJktLMRG5JkiRJkqRaat26dQAc138AAF9q8jkAft5zdJXH7jx7SqW3/X63kwC4oENUV4/GratcT21Q0j79z6jxGZ83XXNm6/Gk2yVL/wLAzM/eB2B0m16x+/4w4KtZqam6VdeaSEXhnq0A5DdsnrE5Vb7H1r4JwM3vvQDAxB5fBOCqLsMqPNavP14IwJ2rXgbgrt5nAjC206Aq1ykFW/fvAeDlTVHKz18+fRc4/Fx+RuueAJzZtjcAbeunL1Gtsu+V0nHO/cfG6HFfuix6DXv8T38C4MILL6zy2JJUGU899RQA37joIgCm9f0ykPvpa7esjBJcHl0XvVdafuI1AOTVbVjmdtsORK9TfV99oNQ+lR3r4o7Re6rJPaOEu/x59yT0Lyy4sczxarPy9oX7qmZbtmM9AEu2rwVgbIfjgMOPG1TvsXvsk7cAGNysEwD9m7artrmOVDM3rgTg0nejJOTKHM9wbnxuw7+LxlyVMPboos8BX2nXF4i/HpV2zi1pnVVWOtZn8uMb//7MhPuv73oiABe06w9Aj8atUh67so+1tp4zP9sXJSif/+6TsduGnDYSgKef+UtWalLNtXHjRiCesJ18uWjRIgD27dsHwOc+F/29ZuTIaE2FlO3it/Xr1w+AOnXqVGvtUnXbsWMHAFdcdjkAjz8RfVd1WpvoNfnOz50CQPdGLTNfnCRJqnVW794MwMQPZwHwj8+i7wO+8fXo++nfPfR7AJo2bZr54iRJRzQTuSVJkiRJkiRJkiRJkiRJkiRJkiRJkiQpzepluwBJkiRJkiRVj1tuuhmAHkflAfDjHqdms5yYX340P+Fy7rDoF/hN5lZNsmzHp0A8vTUo3g59+jdtn7nCaqnklOZcTG+vzc4tSil7sSi9LBynM4oSU1I5f6/atTFh25CIHMaW0iEkcV/77xnA4efwINweLsPavqfPl4CqJXMX7tla6W3T5bTWPQCYePQpAFx79TgAzjzzTACaN2+elbokHVm2bo2fD8N56PbPjQJyP4k7GNgs8XPA+r07AchrXHaK9ns7N5Y79prd0f4rL+03zFlaTVKuC++tpnw0D4D7+5yVzXI4r+0xAPR99QEAXht6BQD5DX1/VVUhdT0kcVfFT1bPAeDRdW+WeH9I5k5O6J7W78tVnrs0YY50uHbF80C8/mT3rXk14XLm578FlP2aUhM+x9REbeo3BmBa73Njt/2f/30cgBkzos+dZ599duYLU8atWbMmdn327NkAzJsXvTbNmROdc5YuXZqwzYABA4B4uvb3vvc9AEaNit4Td+3atRorlmqWkHb5xz9F59DvXnUlANcUfVY85c1pAJzVujcAF7btD0BBi+h50vAo/6m7JElKtOfg/tj1eVui9+tPbVgGwPMb3wOgT+8+ALz8dPT36VNOOSWDFUqSdDgTuSVJkiRJkiRJkiRJkiRJkiRJkiRJkiQpzfyZMkmSJEmSpFpmwYIFADz+xJ8AeOnzlwBQv07dapuzIum5z65/F4Cr3/0bAE9/EiVVjO8+Mv2FqVwmH5dsyda1Ce2p/aLknbBui/epbYnc2VgTIaVZNVPzelGq5HVdTwTiKca//XgRAJN7n1HuGJNWzUpoh7HC2FI6vLxpFRBfo3f1jhKoQ/J7WG8hufvXH78GwC8/mg/E35Nc1WVY2mqa2OOLaR8zVZflHw/AM5tXAPDjO/8bgCl335XxWiQdecI5B6DToUYAXNZ5SLbKqRbdG7VMaK/duw2AHo1blbndwq2FCe2QAFs8hXbJ9vBZo+xE7jBnaTVJue7+ovdrVxSdP/LqJn5+KCy4MaP1hPmfPPZCIF7f5J6nZ7SO2mTxtuh8d+5bf6zyWCHVOyRxX1/0uXNsh4FAPDk9pE+H4xf6r9q1CTj8PF6ZdRZqGf3GIwBM7H5yhcdI9uyG5UA8iXtKr9EAjO1wXEK/eVs+AmDMO08B8EjR46vIOg31Xpk/tAoV1x69GreOXb+uc/TZ7oZrrwfgjDOi70Tq16+f+cJUZYcOHQLg3Xejv1mEdO25c+cC8fTtjz76KLZNONZDh0bPj7POOguAn/70pwAUFBQA0Lp1fN1IShTSMN94+y0AnnjiCQB+N/U3AFw6P/psdBR1AOjdPPobTPu6jQFo5j99lyTpiLOdKIH70wO7AHhv66ex+w4Sva8fcVL0XnzauNsBGDNmDAD16vneQZJUM5jILUmSJEmSJEmSJEmSJEmSJEmSJEmSJElp5k+LSJIkSZIk1RIhPeL6cdcA8O3OUQpjryZtslZTSc5v1w+IJxuHFEwTuVUThJTWm997AYDRbXoB8XX7l0/fjfUNfZKTXqXa6vjmnQH4freTgPj5+7TWPYD48yUIqcjFr4dtw1jKDct2RL9qP3vTh0B20qVTUfwcDTC206AS+4Xz9VVdvgDE1/Kdq14uur3yj++DohS/4Nhm7Ss9VlWF1KL/7noKABf86j4AvnvVlbE+vXr1Omw7SaqK99+PXvN/VXTOAXi6X5RcG85LtUVy+vU726PXy4IW3crc7s7V/wJgdOueCZfFjX9/JnB4ymqyMGdpNUm5KiQKh6Tk22rYd0bHNesAxBOPz23bByj/+V+dQgL0nM3Re/aanqT8m8JFQPyc+OAxZwMw7t8zKj3mku1rE9oXtOsPxJO4g9D+Vsfo80JYZ2/v+AQ4PJG7Ijbs2wnEk7hDanZVxgyeWb88oX1e22NK7Je8Dh9NIZF79e7NCe1sfo6p6b7bKfrO/U9Lo+Nx//33A3DDDTdkrSaVbt++fQC8/vrrAMybNw+IJ22H9meffQZAXl4eACeeeCIAl112GQCjRo2KjXnCCScA0Lhx42qtXToShHTMsWPHJlxu2LABgJdfjr6re/PN6LVs7drotX7btm0ZrVOSJGVf+6L36gWdOgEwaFD8b4Bf/OIXAWjbtm3mC5MkqQJM5JYkSZIkSZIkSZIkSZIkSZIkSZIkSZKkNDORW5IkSZIkqZb417+iBJvX31gCwK+/cGVZ3XPWs+ujtM2Qulk88TWkwX61fZSeHFKUy5OcNBoSOUsbtyJjl1bv1H7nVmic4uYWpStNX/9vAB5Z+0bC/SHx9px2UTJP/6alp+hjABnbAAAgAElEQVR0nj0lof2fUePLnLs6Hk9Q2uMK+/+K/CjxZkTLz1V6jvK8tX1dQvu/Oh5Xajs89rBNqnWlevwgtWNYkTHTuSYqOmdJ8ybPlWoN5T1nIfXzQfJcb510DQBPf7I0YeziadOpnmNCwvvLm1YBhz9vvtXp8wB8t0uUlNajcesyxysum8+XkHIcUowvWfoXAJYPvz6hX7i9pG1TVR2Ps6Ln/LKOc/L6WXjCVQDc+v5LAAxo2g6A8UkJguU9f6Biz9t0e33rfwB46pN3gMPrS06sLu25XBnlvQ6V5Q8Dvlqh/iGZu7Y7vnlnAIa0yAfgl7/4Rey+B/7v/81KTZJqr1/cey8QP+cADMnrlK1yqlVywuv8rR8Dpafgrtq1KaE9unWPhHZIbYV4InfYprQk1zBnaTWV5tkN8VTXkH4bksG/0q4vAOe37ZvSWCE5+W8bVgDx5NfiSeNXdB4CVDytuLyxw7iVGTtVYV8VTwmu7L5KTkwOScTJ41Zk7NL2UXB91yhR9Jw2UWJ0/6L3p6lIdf9Xx77/3X8WA/HnRV7dkt+35c+757DbCgtuLLPPm1+4GoA/f7oMiB+HihzXUE+oL9SbyUTuxduiRMonP40+uyYf++RzUUn7qrKS93FlhP0+rd+Xgfj+r0oid+GexHTOdg2alNm/fYOmCe1/7/ys0nMHD6+NvpcNj2dsh+PK6l4hYV+VZ+bGlQntkHau9GhwVF0ALm8bHdu7J0efh6+77jogni6r6rdjx47Y9fnzo++o5syZA8DcuXMBWLBgQULf9u2j71ZGjBgBwA9/+MOE9uc/H31X53GUsiukaV544YUJl5IkSZIk5TITuSVJkiRJkiRJkiRJkiRJkiRJkiRJkiQpzfzpQEmSJEmSpFriD/8zDYCT20SpXh0aNMtiNaULic5B8fThskxZHaVphATYkoSU23D57x0bgMOTUJP7l5QaW9a4xZWW1HrLey8CJaesAlz97t8AWLtne5lzl1RHefWGfRQunzzu60DVknmr4/EE5R3b5P0f1kxpx7UqQjpuMLgoxbS0dvFtytu/FT1+xa+XdgyruiZSqTtZZecsPm9l12Kqcxfvm/y8LS9F+8YVfy9xu+LtVMe8tihJrKRzB8SfT+HypeMvBcpOXq4Jz5eQ9BjSj8PxWLBlDQCN69Y/bJvQN9WUyMo+Tqi+c355awfgsbVvJmwbUr0rWgNUz7m8uJAYD/Fj9//WvZVQZ0iND8evpHNgLlu1a2NCe2q/c6s85jvbP01ot67fGIivjZvfeyHh/rt6nwnAuUXJj9WZEj6mTX8AfvTIo7HbfvHLXwJQv/7hz1tJqoh9+/YB8Nij/w+AH3UqyGY5GXVxx0FAPA13w76dALStn5gE+/aOTxLag5t1KrMNsLLotSo5kTvMEVJXQw3l+U3hIuDwJOjiYyUnuZaWSjzlo3kA3Lfm1RLvLz5OuB7Socd3K3t9pDp28TlSHTtVFdlX87dEyeiTe55eZr2XvvvXMucsaZ8Fycch1THDPgyXTx4bTzMsLT26ovs/nfs+pEyHsa/rckKVx0x20/svJswRVPQ5ANCvSZRwPv79mUC8/iF5hz+fK2vbgeh9+6tF6+yxT95OqDM8/0Na8+A0zl2d0pHqnSx5zZaW5B4kn6fD9pVZyyHBvqTnWnUr7XwVkrjLWsNB8ueYVvWKPsd8En1GDGs8CGn057U9Bih/X9dGXy36juBHH84C4MX/n707DZCivPf2fxEQRAQEWWRVAVllUXFlcYm4o0KMGolLfDQiyl+ikWjMY/569GiIRo2JmuMSo2KOxqAGiVGMGjYXXFkEN0QUZEfADRV5XhT39FRN90x3T/f0ANfnTU1td/2q6q6qrmaY79PRveWYY44pVUlbnRUrVgAwfXr0XJoyZUps/LXXXitb9ttvvwWgU6fo30YGDYq+GzrttNMAGDAguq67d6/6epAkSZIkSZKKwURuSZIkSZIkSZIkSZIkSZIkSZIkSZIkSSow/yO3JEmSJEmSJEmSJEmSJEmSJEmSJEmSJBVYvVIXIEmSJEmSpMKY9MQTAPys+d41vu22U8blve6INn0rnT/t0w8BuHnRCwCM6XhgbL12DZqULbt4wzoAxn/yZmydg3bqCMDAnXaNtX3m3Amx8Yn9fgzAPk3axqaHdvd96Y6yaefPmwjACS17pK33vk/eqLTeZK3ZSNY7c/+RsTaDV9ctAWDoGw8A8MSKt4GK+5+NYu5PpnM7sv1+ADSp1wCAdd9uAOCOj1+OLX9cy24A9GzUKredSiPUn9zPFtvtEFuu/HhYJtQzuuMBQMXzEeR6/qDqc1jdPpGuzarku83y2w3bXDJ4LFDxHhKmV7XtTNcsVLxuM12zSb0atQTg1m7HAql+GPorwMmzHgJgwvJ5aducvOq92DBT3358xbxYbfctifrf9XscUaGu2nS9BEN27hIbPrB0Vtr5yZ8rU939hMz7mu89P9u+A9CtUQsg+z6c6fqBwt7LIbVfr6xbDKT2C1LnZ3iraB//u8vhGetKJ9P+1naPLJsLpPb/0GadCr6Nw1+9t9L5l777FABPr34fSN17INXfC+X7zaP9+9nb/yybNmPGDAAOPvjggm5L0rZn+vTpAKz9bD0AhzXbvZTl1KjeO8Y/cyz/+nOg4rvEC2s/jo132L5JpeMA/17zAQBDmndOu41MNWSydmP0GWr+AReWTWtcN3reTN78LDpr3mMAPLpiPgAntOgea2P62kUA3PLRiwBc1CF6Dzq/Xf9Ye+s3bwvg9sWvxNY5bueuAPTc/Nk737ZDu9m0nasX1kXn6+X+5wIZvntYNju27aEtom0PaNox1lY4psHEPqcBsHfjNrHpoV2A/V65E4BRb08CKp6HZJvp6gR4bf0nUW2zHoy2vfKdsnnJOvM9/oU89vO+WBEbb12/Ud5tZRLqu7Xr0UBqv8L+nzznb0Dma6Cy+kL9yXObrfJ94JX10ftA6APhPjCsZVTPtZ0OA7L/zL54wCV51aTs3LnkNSB1npLXVzHtufkZcOVu0Wf6cP8KfSeorC8nDXnjvkrnj31vMgCTVy8AKl5P24LwvtZ/p/YATJoUHe9jjjmmZDVtaT74IPqcM23aNACmTp0aG583L/re7Hvfi7KKevfuDcCgQYMAuPjii8vaCtPatq34HaEkSZIkSZJUG5jILUmSJEmSJEmSJEmSJEmSJEmSJEmSJEkFZiK3JEmSJEnSFi4kV6xYvQqAvrvuUspyqlRZonY65ZODq1ovTAvLhITWTCmmyQTRld98AcBbny8HYPFXURLS65sTrLIx49NFWdUbxk9q3StWa2VCYmhI+Z24eb9C8k6fHaNzH9JlC5GQWsz9SbadTNwNwniYH9qesiZK7i1EwnBIqA2+n0i8SycsE+oJbbRrmb5P53r+oOpzWIo+UYptBlVds5DfdVve2e32ASr2w3QpyOEYJP17cyJVVW2GhOdskp5r0/WS9OtOhwAwcOZdsenT9j0n57aqu5+QeV+Lcc9PGlBFWna21w8U/hoKCePB7T2Glv2cTR/cmoxbGKV8hX7zzD5nAYVJwL56wXOx8UzJ78HjK6KEsZCQ/tya1P2j0OclJMO237FZ2bSXX45S7U3kllRdM2fOBFL3mGQa9dasxw7x9OGQqBtSf8NnjvuXvgmkUo6TqaXlx8MyIen45x0PAlLHtXxqL8Bu2++UVa1nt9kr7bahYup3SOhOmr72o9h4Mq05KD8elgn7M/XT8HktmcidW9thfjZt5yok21b63UPr3rFth7TrZBJvMgk59Tk0Sm8O5/P1z5ZmXV84X+E8PbF526nPla2BVDJ0NmnM+R7/Qh77yYl3qWzTpnOR6TpInrdM10B5yfpC/SNa98mrtpDEXt5t3Y4FcktTVs0Jqfehv9zb48QaryH03TA8b/O1OX7ZLCCVzB2eIenSwq9e+J/Y+MQ+pwGZ0+UfXzk/1vaza6Lvp7fFftq7QQsAZr7wUokrqR2+++47AObOnVs2bcqUKQBMnz49Nr54cfRdaoMG0f143333BeDEE6Pr6Le//S0AAwcOBKBp06ZFrV2SJEmSJKkqr7zyCgBPPvkkkPqeY86cOQCsWbMGgA0bNpSguq1T+O6oWbPo3yD33HPPsnmDBw8G4Oijjwagf//+1GYmckuSJEmSJEmSJEmSJEmSJEmSJEmSJElSgZnILUmSJEmStIV7//14QtDuDZtlWLJ4MqWF3vHxzLKfX9ic7HRcy25A9qlK933yRmw8m/WSy4Q2rt/jiLTLJ5M5qyPZRlX1dmrYPOu2f7FblD4SklyTqZ8h6fXczem/6RKEc1XM/Um23X3GLdkXRmr/R7bfN6f10glJqMHQNx7Iu41MCaq5nj+o+hyWok+UYptJhbxmkwqRHpm8bxWizdp0vSRluu5zuR8E1d1PqHpfS9l/sr1+oPDX0Mz9RwLwyroo8ar8fW/C8igVenir6P7Vv0k7IPtnddsp4wpSIxQugTydTEnchUyqz7X+8MwI5yOci/LzCq18auuCBQsqWVKSshfeS3dvkF0y9Nakdf1GsfEFX66JjS/6am1sfK8dd6myzQFNOwCppOPQRviskdxGtonchfhcGmoKur/4h5zbCMmv57WLJyMUs+1cdcriu5XkZ6WQun5958PTLj9uUZREmtzPfIztOABIpQAn03RDYve5bfcG0ifwJlX3+Bfi2GeTgl1dhbgOMqlu/S/3P7fs51fWLwFSiceProgSkIe1jBKP+zduC2T/mb3d9BurVVt52SS8byseXj43Nn5A0/YlqqSi41tE38GOfW8yAHcueQ1Ifz/I9ZyG5O1k/9wWE7nDM/iRBa+WuJKaEdKkXn012t+pU6fGhjNmzABS6VMATZpE96kBA6Jn16hRowAYNGgQkEri3n777YtauyRJkiRJUjY2bdoEwF//+teyaddddx2QSt7u0CH6d5y+ffsCcNJJJwGp70Hq169fM8VuA77++msA1q1bB8DChQvL5t15550AXHnllUAqrfvyyy8H4Ec/+hEAderUqZFaq2IityRJkiRJkiRJkiRJkiRJkiRJkiRJkiQVmInckiRJkiRJW7i1a+PpXo3rNihRJRWd1LpX2c8hdTSkkE7b9xwgv8TWQhn/SZSWFZI5z2jTD0ilhjffriEArervCECfF3JPAyukkBga0j7f+nw5AFPWfAhUPMYh2TUkwBYycXRrEY5hMdpMHu9czx9UfQ6r2yfStVmVfLdZ2X5kq6prFmrfdavaozbc87O9fqDw9/KQ1NeuZTQ8tFmnsnkvrf0IgAeWzgJS6dDhGH2/ebTsXk2i1L9iphgW0spvvgDgnsVRUtjcz1cAteMzSCblnwHF0qRO6q9/h79aLUnVtX79egAa19muxJXUvGQabjKVeOa6xbH5PRu1rLLNbju0iI2HNvZu3Ca2jUw1qHYZvyz6jBUSr0/fJUroGNqiKwDN6oXPoal0974v315pm6EfhRTdtzZ/zpn66ebPlZv7SEiIDgndIcm7fBuqPcpfy+Hnw5rtDsCLaz8GYPyy2UAqCTn0p+9vXm6vzfeJLeUzeyFd1OEAIHWtrd8YJQdn+q40zE+un43wrnH/0jdj69am72WTtRQz8b6Ybdd2TepFx3nt+q3j3Sp8pps+fXpsOGXKFABmzpwJwJdffglAmzbRPWfgwOg7i6uuugpIpW0D9O7dG4C6desWtXZJkiRJkqTqePXV6PcKLrjgAiD1PQjAkUceCcBFF10EwB577FHD1aky7777LgATJkwA4PTTTwfg97//PQB//OMfAdhnn31KUF2KidySJEmSJEmSJEmSJEmSJEmSJEmSJEmSVGAmckuSJEmSJG3hNmyIp8fUrVOnRJVUVD795+E+pwBw8qyHALhqwfMA/KXX8ErbCGmg933yBgCLN0TpJpUlji34cnXaNpIuffep2Pj1exyRdrl1325IOz2dMR0PBFKJr6GWTKmfYX/yEVJZw3Do5lTZD75cA6SOdUj3DAmwuSjm/iTP7fyDor9aGpJsasITK96Ojc/cfySQW6Jd2Od9X7oj1mZVqblVnT/I/Rzm2ieyabMq2W6z/Hbz3Wa21yzkdt0WWrJvh6Su6iSi1YbrpSYUcz+Lcc+vrkzXDxT2Xp5O+WMaUr/D8NV1SwD427I5AJw5d0Js3WQNhaqpUELS+W8WTgOg1+a0yRu7HgUUN50wHKtwvqrqw8n+lukzSyE1qGMKmqTi2ZbvMckk2PCeEJKRQyJyNu8a4VkV1gltHLc5wTm5zZoU0n9DCu38Ay4ECpNCW8y2c5Xddw9rYuOZzsfY9ybHxq/vfHja5ZLpwLkI6dphGPrKwq8+BeDkOX8D4qm5Ic07qA3HP1nDlibUX0jh+If7QRi+tv4TAB5ePheAs+Y9FlsveX6T41ujbjvsHBtf8XX0Lt64Yfo+/NFX8e+w2jVonPW2Fn21Nja+1467ZL1uvsI5DtdxVddo+C4iqKx/5tp28n5VjL6/pdn43XelLqFKS5cuBWDatGll08LPU6dOBeDNN6P778aNGwHo2jV6noTE7Z/85Cex8S5duhS7bEmSJEmSpKL67W9/C8Bll10GQJ8+fQD405/+VLaMCdy1Wzg/v/jFLwAYPjz6fdSQxL3ffvsBcP311wNw6aWX1nSJgInckiRJkiRJkiRJkiRJkiRJkiRJkiRJklRwJnJLkiRJkiSpRgzcaVegYtpqSKwMKaBJx21OJw3Lj/8kSgUZ0SZKeimfjhUSsx5ZNjfWxvebd8qqxmTadEjJvOPjl7NaH+CgnToCqQTrkDz+310Oj9Ubag37k43L3n0aSB2Lif1+DMA+TdrG2i6kYu5P8tyG43x2u32Aiomp0z79EEil017Z6VAARrbfN+ttBiGVKOxX6Jf5HMOwTmgjtJncj2Kcv1L0iZrYZrj2qkphTpcQn891W2gH7tQBSB2jexa/CsDI9tFfeA379fiKeQCcP28ikOpD6ZKiS3m91KTq7idkv6+FuOfnqhTXbD5CPWF4Rtuob05Z82HJaspGeBYd/uq9AIzpeCAAY3cbVGM1DG/VA0h9vnluzQIATmjZI+3yYX5wXLlUdknSliWZBPvEyndi48Nads+5zbBOSEhNtpncZk0YujnpOSQm3774FQDObrMXUPHzGsD0tYuAVCr0lbsdDMB57fpXq+3QbjZt52r8stkAjGjdG0j/3UNISg8GNO2QVdshybtTw2ZAKtk27G82Lnv/GSB1rCb2OQ2AvRu3qVBvtvI9/oU89r13bBUbzyYZvZRCfUGy/mIK5zoMz9iciDz109r9mb2Y9mgYvyc+suItoOJ1HM7bE6vi99S9dmyT9bbmfbEiNt653PcSxZJ8Jvxj5dsAjGjdJ7ZcuKf8fflbsenhGs+m7WfXfADACS3SP7vC/GzaVs15773oPTSkbE+ZMgWA6dOnA/DOO1Gfr1u3btk6fftG945Bg6L35l/+8pcADBgwAIBddil+2rwkSZIkSVJN+uabbwAYNWoUAH/+858BGD16NAAnnngiAHXq1ClBdSqEkNB90003AfDYY48BcPnllwOp78luu+02ALbbbrsaqctEbkmSJEmSJEmSJEmSJEmSJEmSJEmSJEkqMBO5JUmSJEmSVKNGdzwASKWRnjl3AgDzD7oIqJjAG5K8Q6pmSDsOw8qEdTKlfd/eYyiQSsMdOPOuLPciJZnomqnekMyZ9Ns9jsx6Wz9svSeQOnZD33ggq/Vy2UZSMfcn33MbzudJrXtlva2k19ctiY2HtNnqCG2E8xO2EerN9/xB5uNaij5RjP0Ixyj0q+4zbgEqJlQX45othpC8O2F5lLidbd+urB+W8nqpSdXdT8i8r9XtP6HvQP79pxjXT03o2ahVbFhbPb86ngqXy2eG8pYMHpt2etsp46pc9tBmnYBUnwz9LQwzCX0+XAOSpC1PhwZNY+PJtOZkUmw2ejdqXWmbyW3WhAFNOwJwUYfo3f6Wj16MDSszpHlnAH7QqmeNt52rdg0aA7DfK3dWuWyoN9SfdFu3YwEY9fYkAAa9dk/O9SRTvE9uFX3mDenZQ2c9mFU747oMyTgv3+NfyGOfTERe9vXnQO1N5A71BbkkOhdaz0YtY8NtUdj30Cez7cOnb04zz+XYzf5seWw8+X1mrtpNv7HCtMUDLomNh3TsR1fMB2Dse5Njw0yqukcBHNZsdyB17ML9Kgyr07aqb+PGjQDMmjULSCVuT506NTZcunQpAA0bNgRgv/32A+Dkk08GYODAgQAcdNBBZW03bty4qLVLkiRJkiTVFps2bQLg1FNPBeDpp58G4Prrrwdg3333LU1hKpqQqj5s2DAA2rdvD8BVV10FwOrV0e8BPfLII7Hli8VEbkmSJEmSJEmSJEmSJEmSJEmSJEmSJEkqMBO5JUmSJEmSVKNCitJfeg0HUoncDy6NEkVGtk//1y3H7jYIgG6NWgCppNvyydAh/XJ4qygNN6TiZhLmf/bt1wBc+u5TsfkhHTOku3713bdl8w5/9V4AXvj0I6BiOmtV9YZk2FBDctvp7NOkLQDP7HMWAE+seBuomDQa6t6rcZQElSmRPBfF2J9MbYdjGhJrg5BGe2SLPQBosd0OOe5FygOb+1s4NoVImQ1thDaT28j1/EHV57AUfSLfbVa23V/sFiUCtam/I5A69598/Vlsuaqu2fLbTV63VV2zxXDr5uS9iZvTsqq6x2RTUymul1LIdz8h877me89P9h3Iv/9ke/2Ur6eQ9/KtXS7PnmIJSXzh+n9uzQKg4nPzjDb9ADiuZTfAJG5J2hq0rt+o0vn5pOSG9OV8t1lMYzsOAKDbDlHS+AtrPwZSCdHlhRToI5tHn2eq+myabdvl06WzbTtbI1r3AWDHuvWBeCptSKwd1jJKxw0puZmE+Z9tjD6HJtNzQ6LtSS1Tidbhs+iQN+4D4IV14T0m6hN7b/6MOLnfGQA8seodoGLycGh7rx13idVemVyPfyGPfTJR+ZnNn6X2bly6pOvKhPpCvdtyGnZtckOXIwB4anX02Xvy6gWbh+8DqfM1pHknAI5v0S3nbSSvh5p85763x4kAPL4y+q4hJHSH/QsJ40NbdAWyS8tuXHfze0zXowF4ds0HBWtblfvqq68AmDlzZtm0KVOmADB9+vTYcN26dQA0axY9CwYMiO7XY8aMAWDQoOi7lP79+wNQv379otYuSZIkSZK0JfnlL38JpJK4b7nlFgA6d676e3NtHULqejj3F110EZDqG9ddd11Rt28ityRJkiRJkiRJkiRJkiRJkiRJkiRJkiQVWJ1NmzZtKnURkiRJkiRJyt+DDz4IwIgRIwBYMnhsKcuRJElSLXbB/CfKfm54eJQMPn78+FKVI2krEd5Hv5z8OgB/6HpMKcuRtIWbvnYRACfP+RsA8w+4EEilBpfa+o0bAOj+4h8AeHjPHwKmE0vbokdXzAPgwnf+CUDyVzHXrl0LwLRp02LDqVOnAvDKK68AsGHDhrJ12rVrB8DgwYMBGDhwIJBK3O7VqxcA3/ueGT6SJEmSJElV+dvfou+Zf/SjHwFw7bXXAnDAAQeUrCbVDi+++CIAV1xxBQB//etfAfjhD39YlO35bZ4kSZIkSZIkSZIkSZIkSZIkSZIkSZIkFVi9UhcgSZIkSZIkSZIkSZIkSZBKtj59l74APLvmAwBOaNG9ZDWVF+oJ9ZnELSkYPXo0AFOmTAFgzpw5QCqpu3v36D4WUrbPO+88IJW2DbDbbrvVSK2SJEmSJElbs3Xr1gFw4YUXAnD++ecDJnErJfSF0DdCXznyyCPLlmnSpEnBtmcityRJkiRJkiRJkiRJkiRJkiRJkiRJkiQVmInckiRJkiRJkiRJkiRJkmqV0e33A2C/V+4E4LBmuwPQuG6DktSzfuMGAEa9PQmAl/ufW5I6JNVeL7/8MgCHH344AFdddRUAAwYMAKBly5alKUySJEmSJGkbc8011wCw8847AzB8+PBSlqNaLPSNf//730Cq7wCMGzeuYNsxkVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmSCsxEbkmSJEmSJEmSJEmSJEm1SrsGTQCY3O8MAP6x8m0ARrTuU5J6wvZDPaE+SQpeeumlUpcgSZIkSZK0TXvvvfcAuOWWWwC4+eabAfje98xDVnqhb4wePRqAMWPGlM376U9/CkCXLl2qv51qtyBJkiRJkiRJkiRJkiRJkiRJkiRJkiRJivE/ckuSJEmSJEmSJEmSJEmSJEmSJEmSJElSgdUrdQGSJEmSJEmSJEmSJEmSlE7PRi1jw1IZ0bpPSbcvSZIkSZIkSarcTTfdBECvXr0A6NmzZynL0RYk9JXyfebmm28G4A9/+EO12zeRW5IkSZIkSZIkSZIkSZIkSZIkSZIkSZIKzERuSZIkSZIkSZIkSZIkSZIkSZIkSZIkbXG++eYbAB544AEARo0aVcpytAU76qijyn6+7bbbgFTS+3bbbZd3uyZyS5IkSZIkSZIkSZIkSZIkSZIkSZIkSVKBmcgtSZIkSZIkSVuItlPGxcaXDB67VW6zJqz7dgMAE1fMB+Dp1e8DMHnVewAM2bkLAMNb9QDg0GadytZtUq9BjdRQWR3VreGtz5eX/Xz4q/cCuZ/bx1fMA2DC8mgY6j6jTb9o2LZf2bI9G7XKu1ZJkiRpW9Nu+o2x8cUDLtkqt1kT1m+M3rv+sfJtACavXrB5GL1/DWneGYBhLbsDcFiz3QFoXLcw73351FCIOkLbZ817DMjtfCb7Qr62lj4kSZIkSZIkqfabPn06AOvWrQNg//33L2U52oKV7zvXX389ADNmzADg4IMPzrtdE7klSZIkSZIkSZIkSZIkSZIkSZIkSZIkqcBM5JYkSZIkSZIkbXP++4P/AHDfJ2+knR/SpZMJ3QB/6TW8RmqorI58a1j5zRdAKoU7H2fOnRCrKQxRDaUAACAASURBVCnsT/n9ur3HUABOaNkj7+1KkiRJUq6uXTgVgPuXvpl2fkiuTqZj39vjxJLVUJ063vp8BZBK4i6FULskSZIkSZIk1ZSZM2cCsMsuuwDQrFmzUpajLVj5vhP608svvwyYyC1JkiRJkiRJkiRJkiRJkiRJkiRJkiRJtYqJ3JIkSZIkSZK0hVgyeOw2sc1ieuvz5UAqLXpMxwMBGNGmLwDtGjQBYPGGdQDcuujF2PIAC75cDUCnhs2LWkNldeRbww0Lp+VVM8DjK+YBqSTuKzsdCsBpu/QBoEm9BmmXBzh/3kQA+jdpB8T3UZIkSVLc4gGXbBPbLKaQTB1SsC/qcAAAI1r3BtK8+338cmz5BV+uAaBTw/xTW/KtIZ86Xlv/CQBDZz2Yd71Brn0h7OeQN+4D4Mrd8k8kkSRJkiRJkqR8vP/++wC0a9euxJVoaxL604IFC6rdlonckiRJkiRJkiRJkiRJkiRJkiRJkiRJklRgJnJLkiRJkiRJkrYZr6/7JDZ+UuteQMWE6DB+Rtt+QDyRe/Zny4D8E7mzraGyOnKt4Y6PZwLwydef5VFxZMLyebHxTEncwaHNOlWY9vzqD4BU+rgkSZIkFcPrnyXeu1r2BCp599slekcJSdizPw/vXPkncudbQy51/GnxKwBcvfA/ANzW7VgARr09Ke+6s7Xymy+AVBL3uC5DgOodM0mSJEmSJEnKx/r16wFo1KhRiSspjpA4Pm9e9Ls7xx13XMlqeeKJJwDo0aMHAJ07dy5ZLcUW+tO6deuq3ZaJ3JIkSZIkSZIkSZIkSZIkSZIkSZIkSZJUYCZyS5IkSZIkSVIRPb4i+kuoIc148qr3ABjT8UAglcY8cOZdFdZdMnhsbLztlHE5zZ914IUAPLJsLgBXL3gOgCE7dylbZnir6K+jntCyR9r6q9pmLpJt5as6NSzeEP8LqS22q/wv8baqv2OFaW9/vjL6oWXN1JCujmxrmPbph0Dq3D+zz1lAqh/mIrlOpiTuyuaHJHFJkiRpa/P4yvkAPLoiGk5eHaVjXNThACCVxjzotXti6y0ecEmFttpNv7HSZZLz39zvfAD+vvwtIJXOPKR5lIIxrGV3AE5o0T1j/VVtM1vJdqoj3xoAFm9YHxtvWX+HSpdvVT/+Xvb2F6vy3nahasimjnCu7+1xIpA65zWRyH3PJ6/HtjmidZ+ib1OSJEmSJEmSKlO/fv1Sl1BQy5ZFv2dz9913A3DFFVeUshwADj30UACOPfZYAB566CEAWrduXbKaiqWQ/clEbkmSJEmSJEmSJEmSJEmSJEmSJEmSJEkqMBO5JUmSJEmSJKkIxi2cCsDNi15IOz9MzzS/EC55519AxSTl8uPJeZmSubcWyeNdVap0i+0qJqaFNsbuNqhGakhXR1U1LPhyNQAnz4r+6u3tPYYC0LNRq9yKLSckuYc+s+7bDUDm+sP88u775A0Art/jiLzrkCRJkmqTcYumA3DLRy+mnR+mZ5pfCD9/72kglQIehPHkdKg8nXtrkDzejevm9u4X1h/bcUDJasimjuqkludr+tpFQKq2h/f8YY3XIEmSJEmSJEnbgvHjxwNw0kknAdCoUaNSlhOr4Xe/+x2QqvHiiy8uWU1bAhO5JUmSJEmSJEmSJEmSJEmSJEmSJEmSJKnATOSWJEmSJEmSpAKa9umHQCoxeUzHAwEY0aYvAO0aNAFg8YZ1ANy6KEqwCknFhdSrUctoG92OBVLJyaFGSCU2T1g+DyhuIveSwWOL1rYiIQX7qgXPA6n+V4jzOrxV1EZI5H5uzYK0bYca7vj45WpvU5IkSaqtkqnEF3U4AIARrXsDad79Nn8+vn/pmwWvpWd49+t6NJBKfg41njznbwA8umJ+2TrFSuQuRUK0atadS14DYEjzzgAMaNqxlOVIkrZQ8+dHn0umTp0KwJw5cwBYvXo1ABs2bChNYZIkSZK2KA0aRN+FNm/eHIA999wTgEGDBgHQvXtxvgeViu2116LvYf/xj38AcN5555WynLS6desGpJK4DznkkLJ5e++9dylKqtVM5JYkSZIkSZIkSZIkSZIkSZIkSZIkSZKkAjORW5IkSZIkSZIKaMani2LjySTuIIz/tH1/oDiJ3Ge32wdIJXEHA3fatcKyIWVZW7aQgh3O541djypY24c26wTAkJ27AHD+vImxYVJIA5ckSZK2RtPXfhQbTyZxB2Xvfm2j97NiJHKf3WYvIJXEHSSTkievfr/g29a247X1nwCpfnRvjxNLWY4kaQuxfPlyAO644w4A7v3znwH4YOFCAHZqGn1W6tm1MwDNNo9v36B+TZYpSZIkaQu1Zs3XACx4ey4A9/3lXgA+XbsOgN132w2As37yEwBGjhwJQKtWrWqwSil3jzzyCAA///nPAWjUqFEpy0kr1BRqDDWDidzpmMgtSZIkSZIkSZIkSZIkSZIkSZIkSZIkSQVmIrckSZIkSZIkFdDNi16IjSfT2JI6NWxetFpabLdD0drOR9sp4wrSzpLBYwvSztbk8RXzgFT/m9jvx0Bh+0BIdg8p30+tfBeAS999CkgldQ9v1QOAE1r2KFs3eV1IkiRJW7pbPnoxNl71u1+zotVSm9792k2/sWBtLR5wScHaUvU9vHxubPyApu1LVIkkqbb6+usoCe/WW28tm3bNf/0XAA3qbwfAmT88HoAfHDcEgL49u9VkiZIkSZK2EW/MnQ/AhEnPAPDHW38PwE2/+x0Av/q//xeA0aNHA1C/fv2aLlFK66233gJgxowZAPz4xz8uZTlZ6dSpEwA33HBD2bSwHz179ixJTbWRidySJEmSJEmSJEmSJEmSJEmSJEmSJEmSVGAmckuSJEmSJEmSthljOh4IpBKi1327AUilTSeF+enaqKka0tWRrOH8eRNj40PfeCDnupKJ6ZmSz0Pa34g2fWPDpMUb1lWYdmWnQ3OuS5IkSZJydVGHA4BUcvr6jdE7VeO66d+7wvzk+qWsoVB1VNfKb74A4P6lbwKpmjLthyRp2zN79mwATjn5ZAAWLlxYNu+ic0YAMPaC/wPADg23r9niJEmSJG2T+vXqHhteNvocAMb98W4A/u+vfgXA3XfdBcBDDz8MQO/evWu0TilpwYIFsfGdd965RJVkL12NYT9M5E4xkVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmSCsxEbkmSJEmSJEkqoGTackglbtegSdrl06UWb60yJTzXpG6NWsTGV37zOZA5DfvjDWsrTMt0LotVQ7o6qltDPs6cOwGAyaveA2D+QRcBmev+4Ms1Faa1abBjkaqTJEmSalYybdl3v8jiAZeUugQAuu0QTwBZ8XWUKt24Yfr3l4++ip+fdg0al7yGQtVRXYu+ir+P7rXjLiWqRJJU2zz55JMAnHJKlMTdv08vAB6765GyZXZt37bmC5MkSZKkhB0abg/A///zCwD4yanDABj5i/8CYMCAgwB46KEomfvoo4+u6RIlAGbMmBEbb926dYkqyV66GsN+HHfccTVdTq1lIrckSZIkSZIkSZIkSZIkSZIkSZIkSZIkFZj/kVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmSCqxeqQuQJEmSJEmSpK3JQTt1BODmRS8AMP6TNwEY0aYvAO0aNAFg8YZ1sfmqGXvssHNs/JFlc4HM5+eJFW9XaGOvJm1qpIbK6kjWsGTw2JxqaDtlXIVpVbUxvFUPACaveg+AiSvmA6m6gwVfrgbSH7v+TdrlVKckSZJUWw1o2gGAWz56EYDxy2YDMKJ1byDNu9/m+aoZezRMvHeteAvIfH6eWPVObPm9dqzee18haihUHdU174sVsfHODZuXqBJJUm1x1113ATBy5EgAzj51GAA3XfULAOrVq1uawiRJkiQpS7u2bwvAxL/8AYCf/fo3AAwdOhSAO+64o2zZc845p4ar07ZsxowZpS6hILaW/SgkE7klSZIkSZIkSZIkSZIkSZIkSZIkSZIkqcBM5JYkSZIkSZKkAhq4064AjOl4IJBK5g5DlVbPRq0AGLJzFyD783NGm34V2sgkmXadTLrOt4bydVRVQzEc2qwTkKr70nefig0rc3uP6K9Wl08blyRJkrZkA5p2BOCiDgcAqWTuMFRp9WzUEoAhzTsD2Z+f03fpG1u/Mu2m3xgbXzzgkoLUkGsdxTb7s+Wx8Sb1GpSoEklSqT377LMAXHDBBQD819gLAbj4vDNLVpMkSZIkVUe9enUBuPXaXwKwe8d2QOq9B6BTp+h3JQ477LAark7S1sREbkmSJEmSJEmSJEmSJEmSJEmSJEmSJEkqMBO5JUmSJEmSJKkIxu42CIBujVoAMGH5PAAmr3oPSCV2n9S6FwADZ95V0yVu027sehQAT618F4CnV78PpM5PSJ0+YnNy2tCW3Wu8hpqqI1shdS1ZdzKRO/Tt41p2K5tWigRxSZIkqSaM7TgAgG477AzAoyvmAzB58+f7kNh9UsueAAx67Z6aLnGbdkOXIwB4anX0njV59YLNw+j8hLTsIc2jVJ3jW3RLNlHjNRSrjnzdv/TN2HiL7XYoUSWSpFJ55513ABg+bBgAo848BShsEvfa9Z8B8NTz0wGY+uKrANw5/pHYcpePPgeAgw/ct2zaIQftSzY+WrIUgA5td6lesVuB7XfbOzb+1cLXSlRJzUrudyFsK8euNsjn/B17+OBo+P1oOPSIQwBouXPzgtWl4sn22QAVnw/ZPhtUfcV8ptgHiivbc1fVZ6h092efj1uHcA3+/YmnAZj07ynR8JloGJ6zp5xwNABHHjKgbN2mjXcsSA3V+fyWbz8M7znLV64umxbehV6eOROArl275l2XVJXjjz8egH/84x8lrqR6wn4oxURuSZIkSZIkSZIkSZIkSZIkSZIkSZIkSSqwOps2bdpU6iIkSZIkSZKUvwcffBCAESNGALBk8NhSliMpT22njAPgjDb9yqZdv8cRpSpHkrSVumD+E2U/Nzw8euaMHz++VOVI2kqE99EvJ78OwB+6HlPKcqRard30GwE4fZe+AFzf+fBSliNJqoZHV8wD4MJ3/gmAv4qprU3o0wcPjpL2mjasB8BDd9wAQN261c+SWrEqSrkb+YurgVS6Xy5CEuA9N10T1ZlI/7v5zvsBuOzamwATIsFE7kLaVo5dbVCI8xfuF3f85krAZO7aqiaeDSqcYjxT7AM1o6pzl+1nKBO5t16jr/hvAO4c/0hWy4frDuDvd91crW2HJPg9Dsr/3xqq2w83bvyu7OdTRv4cgLVffgvAf6ZE96U6depUaxtbs40bN8aG9evXL/o2w79VLV++HIBf/epXRd9mMTzxRPTv+TfcEL17P/TQQwC0bt26ZDVVZdmyZQCccsopZdN+/vPoujnuuONKUlOhXHNN9FmiVatWQPV+t8JEbkmSJEmSJEmSJEmSJEmSJEmSJEmSJEkqsHqlLkCSJEmSJEmStiYhWTuY2O/HAOzTpG1s+rpvNwDw4NJZsekH7tShiNVJkiRJkgohJGsHE/ucBsDejdvEpq/fGN79ZsemH9i0fRGrkyRJqr6//vWvALw1dy4As5+dABQmiTsYP2ESkEravO26KDFt6BGHABXTckNC5233/m/ZtOtuvQuAp56fDsDJQ4+MrRNSJJWyraZkVrXf22pS+ZYqm/Ozdv1nANz8P/cBqfvFczNmAhXvF6odcn02QMXnQ1XPBtVu9oGaUdV9NNvPUD4vtz6z5r0DpJK4Lx99DgBn/2g4AB3a7gKkUrPH/fGe2PIA737wIQB77L5rQWq6/oqfATDm3NML0l42yr/3/M+4XwPQ+7DoGIR3pdNOO63G6tnS/OQnPwHg/vvvB2DXXaO+0K9fPwB69uwZG/bo0QOA7t27A9CoUaOaK7aWCcciWLVqFVB1IvchhxwSG3/++eervWy2y4Uay0vuh0zkliRJkiRJkiRJkiRJkiRJkiRJkiRJkqSCM5FbkiRJkiRJkgroL72iv8B75twonWToGw9ktd6QnbsAcGizTsUpTJIkSZJUMPf2OBGAs+Y9BsDQWQ9mtd6Q5p0BOKzZ7sUpTJIkqZq++OILAC6/7DIAfn3JSACaN2ta8G0lkx5Dyl8mIYFzzE/PKJsWEjfPGH05YOKmpJSmjXcEUveMcL946PEnAe8XtVWuzwao+Hzw2bBlsw9oaxWSrp+d9hJQs+nSuXjljTmx8dOGHwukkriDMH7uj08C4oncr8+ZD+SfyP3+wo9i4/16dc+rnUIJ70Lh3Si8K514YvQd8Q477FCawmqxhg0bxsY//DBKaV+0aBEA//rXvwD45ptvAPjuu+8AqFOnDgBt27YFoHfv3rFhSOwG6NWrV4VpW4POnaN/QzjooIMAeOGFF4BUenltFGoMNUNqP5RiIrckSZIkSZIkSZIkSZIkSZIkSZIkSZIkFZiJ3JIkSZIkSZJUQCFZ++E+pwAw49Por8nevOiF2HJntOkHwIE7dQBSSdxN6jWokTolSZIkSfkLydoP7/lDAKavjVJibvnoxdhyp+/SF4ADm7YHUkncjev67idJkmqnO+64A4BN320E4P/86AdF29a5I+LpfStWrQZSqZqZhJRdgK8WvpZ2me132zur6ZnWf37GzLKf/z5pcqzOYw8fDMDos0cAcMhB+1Zab6a2k+3m03Zyf96d8U8Axlx5PQB9enQF4NeXjMq4TvIYJOd/9OozAIyfMAlIJaWGWk854Wggt7TThyc+BaSSkSc9MwWAy0efA6SSH3sfOqzSWmuLH5wzBoC2rVsBcOu1v6x0+bXrPwOgde/oGIZrofy6mc5TOHYhZTa479brADjykAFA/DqpSqY+WZ2+nu21VhOSxyL0t6qEYw0V+2pV/f/l12cDMHjYmUCqb5e/FpPe/SBKqgz9/uUn/xdIXcdJo6/4byB1vjItnzy/5dcp1j0HKt/XyuT7bIDUua6qv+V6fUH+11i21xcU7nlS3ecUZL5Ph+NQzITrmugDScV8Nmf7HIWqj2tldULqXjPsmMOBzPePdHWGY5brZ6h0yy+bHfWX5HOuqudjkLy/hfbSXXfFvA6qKzwL7n9kYqy2IF0id6bjn6vqPHc/WrI0Nt6qxc6VLt+mVYsK0+a9837e26/NwrvRb2//C5B6d7r44otLVlNttf/++wNw9913A7BxY/R+uWnTJgA2bNiQdr0wf/HixbHhs88+G2sn+XN5ffr0qVbttcVJJ0X3ztC/Tj31VAAaNWqUdvnnn38+67azXbaq5T7//HMA7r//fgB+97vfZV3DtshEbkmSJEmSJEmSJEmSJEmSJEmSJEmSJEkqMBO5JUmSJEmSJKkIBu60a2w4drdBpSxHkiRJklQEA5p2jA3HdhxQynIkSZKq7X/+9CcAfnLKCQDUrVu8zKhzfxxP3Bz5i6sBOPvU4QD06RmlSHZou0vRaki66sbbALju1rsyLhNSSZMp0lWlzxaz7eCev06IrV8+5TNf4bwkU4yTtZaXKVG0qmMQpld2jGqjkPZ51GnnAam+nSkJ9e33PoiNH3XowCq3EY5zuqTg8tNDAunf77q5yjarOh/V7Y+1RUhAD8IxyiSfazUkj4Zj063L7rHlQ1uVHbvX58yPjb/yxhwgcz9KJrsml9tS7znZPhugcM+Hqq6v8vOyvcZyvb6gcM+TTOc1m7aTSchJ4TgsWbq80naqoyb7QE1cJ4V4joZlfnDOmEq3lXyO/uvB6HNdTadRh+Ts66/4GZBKIb/y4pFA5nT1kL4ezn1YP5nEHc4b1I5nWHjOTHspSsG+53/j98aQSB7uG/vttWfBayik5DFNl4ReXrrzmc1zrzJvzI0/E5s3awqknjujLr+mwjq3XfcrAH5w3BFA1XXnI7wbnXXy8UDq3clE7or69u0LZE7NztXXX39dYVqdOnWAVIp3kCmxekuz9957A3D88VF/e+mllwA47LDDSlZTUqgp1BhqVnomckuSJEmSJEmSJEmSJEmSJEmSJEmSJElSgZnILUmSJEmSJEmSJEmSJEmSJG3D5s+PUu/efucdAI4/omLKXaGF9NjZzz0KwO/vGg9kTpsMqYwH9e9XNm2/vXqnXfarhVEi4va77Z12etLzM2YCqfTA8umlY356BpBK9Qupizf/z32xdYYdc3hsv6pqO9luPm0n9ejaudL9zEfY5j03XROrN+xXSKF+6PEny9ZJJolmOgZn/yhKVw2Jqh8tWQrAuD/eA2ROhK1tQtJpSOp99J/PAJnP14xX3oiNd+28a5XbCOme7874J1DxmIWEynCMwzFPl8KabZ+sTn8sZB/MV0h2ve3e/41Nz5QaXdl9IFNfTR73gw+Mjnc47qGNMP/dDz4EYI/dK57z8tcQpNJGw7aD0EYQEkir2o9wfqEw5xiKe8/J9tkAFZ8PmZ4NmVR1fUH211i+11f5dar7PMl0XrNpO9x3q7pPh+NRDDXRB2ry2ZztcxRS94HkczS578m+Grz8+mwABg87E4C/T5oM5JbInetnqMocNnD/2Phzm/c5uX/J+UF4rgbJ8wbFeYZVJVwHL7z6JpBKqg/1hufMzVdfBuSWHF8bnp+10X5Hn1rlMuG5OenfURJ68porpBOOjBKR/+umO4DUu1T37t0Lvq0tyZIlSyr8/L3vRRnE3333XUG2Ua9e6r/C7rTTTgD85je/AeCZZ6LP4MuWLSvItmqLESNGAHDKKacAsP/+0b21lMnjn3/+OQBXX301AA899FDJaim2QvVdMJFbkiRJkiRJkiRJkiRJkiRJkiRJkiRJkgrORG5JkiRJkiRJUtG0nTKuwrQlg8eWoJJt27pvNwDw3JoFAExYPq9s3uRV7wEwZOcuAAxv1QOAQ5t1AqBJvQbV3v5bny8H4PBX7wWy7wOZ6k7WfETzzmXrHNliDwBabLdDNauWJEmSlK1202+MjS8ecEmJKtm2rd8YvUM9u+YDAB5dEaUBTV79PgBDyr07DWsZJQQd1mx3ABrXrfzdL9u2hzSP3iWPbN6lbN2q3s+S/Sdb9jNJKqwpU6LkuqZNGgOwZ/culS1eUCGZ9tZrfwnAlRePBOCDRYsBmDP/XSCVrnfZtTeVrRtSGH99yahq1fCfF+IJkOmSa5PjYZmQ8PjstJeAigmPmdpOlw6Ya9tJh+aQuJmtUWedGqstSKZ7TnpmSsY2kscgmfAahPH/75wodW5LSeQORp8d1R3SVTPtZ+jDIbkzXTpzUkibzXTMwrZCn6kshTXbPlnd/lhMyaTYbITjfeQhA9LOD8csKJ+Ene9xP/qwQbH577wfT+Qun64drqH7br0OSCW8zpr3DpA63os/WR6rpX+/PWPjW/o9J6jq2QCZnw+5Phuqur4g+2ss3+urfNvVfZ5kOq+5tF3Vffq04cdWaLvQitkHavI6yfY5CpmfpeH+FeaH/tevV/Ruv1fv6N93QxJ5bUl1Dscm1J8pcTwI888dcRJQ8fmYPG9QmmfYHgcdExsP9+1M+6Xclf+8DzDl0b8AqT6ezsMTnwJSz8+nnp8OFOe8hHel8O4U3qW2tkTukLo8d+5cAGbNmgXA7NmzAZgzZw4Ab74ZpdOvWrWqQhtNmzYFYO3atXnVsN122wFQp04dAC699NKyeZddFqXe77hjdL1PmzYtVvfWonXr1gDcfffdADz33HMAHHfccSWrKdQQago1bo0+++wzALbffvtqt2UityRJkiRJkiRJkiRJkiRJkiRJkiRJkiQVWJ1NmzZtKnURkiRJkiRJyt+DDz4IwIgR0V94N+lWUm1iIndprfzmCwAueedfQCrJOhsh7frGrkcBuSdch20D9HnhD7F5VfWBkMQ9+u1JQM3WLW3tLpj/RNnPDQ/vB8D48eNLVY6krUR4H/1y8usA/KHrMZUtLmkrZCJ3aYX3r5+/9zSQSsnORkjSvqHLEUDFd6iQxD36nSdzart8+nemthdvWAfAfq/cmXW9sfXtZyqxR1fMA+DCd/4JgL+KqS3dz34WpZG+ODVK1nr+738uZTmVen5GKo0xJB/fdt2vgHiCLlRM7c2UUJlPum8myW3UZNvZJHBWtU6ubabbv0LUWZ318lHIbf3gnDEADNp/HwDGnHs6kEpW3u/oKJ01U7+tTj3ZrFeoPlmTia/VSeA+9vvRcOgRhwDQcufmWW0jl/3LtO7a9VGCX+veUQ3JlOCQHgqpBNGwbmgz2U9uvvN+IJVWWtvvOTUpPB8K9WxIJ9d7aHVsCee2tvWJfPtAddTEczT5/EgK97vRZ0ff06ZL+65qu/nuRzb1J8/L7OceBVKJ2+9+8CEAvQ8dBsC/HvwTUHE/innecvHRkqUAvPBqlEIc7t/hPJxywtEAHLhPX6Bisn1lasMzulD3g+rWka9QRzgff7/r5qJt6+DhZwFw4ODDALjpppsqWbq0Nm7cWPbzggULgFSSdkjWDknbIXk7LPfdd98B0KhRIwB69eoFQJ8+fQDo3bt3bFh+3k9/+lMAHnvssVhbmdSrVy9W78knnwzAuHHR7x517Ngx47q/+lV0z3/ooYcAuOuuuyrdlpSNc86JPr+fcsopAFxzzTV5t2UityRJkiRJkiRJkiRJkiRJkiRJkiRJkiQVWL1SFyBJkiRJkiRJkorjqZXvAqlE69t7DAXghJY9Mq7z+OY0qfPnTYy1MaJN35y2fcPCabkVW85za6K/7Bzq/u0eRwIwtGV3AJrUawCkkrvv+PjlsnVvXvQCAI8smwvAyPZV/8V5SZIkSdqSPbU6encKadm3dTsWgBNadM+4zuMr5wMw6u1JsTZGtO4TW+7ZNR/E2h7XZQgAx7foBkDjutH7WUjuvn3xKwDc8tGLZW38fflbAJzXrn+l+3HlbgdntZwkqThWrlwJwM7Ndir6tqqb3pkuZXLU5VEqVrpkY6mmnX1q1A9DMveI4dHns/nvfRBbrn+/PWu2sK1IqVN/s9W08Y5AKon7ulujZMiQgamVnAAAIABJREFUyP3Q40+WLRuSg5PjyftbSOK+/oqfFavskihEsnPy+eCzYctiH8isT4+uQOqYhITuZ6e9BKTuC5OemQKkkojDvSasXyp79Y7/2/TUF18FUoncr8+ZX+nytU1I2A7DIw8ZAMC0l6Lzc8//TgBSSd3njjgJgKMOHQjAfntFz/+WOzevoYpzk3xmrV3/GZB6piWF+enaKKVwPRRTi+bNAFi1alXRt5XJihUrgFSKdkjVDinbIXX7rbfeKlvniy++AKBu3boAdOrUCYC+faPfCTn99NMB2HPPPWPTd999dwC+973sc4XDupMmRd9BbtiwITY/tBWSuvfaay8Afv/73wNwwAEH5Lyt3/zmNwB88803AGy33XZZtyFBqu8ALFy4EEj1r+owkVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmSCsz/yC1JkiRJkiRJkiRJkiRJkiRJkiRJkiRJBVav1AVIkiRJkiRJkqTiuPTdp2LjJ7TsUeU6YZnz502MtTGiTd+stnnHxzMB+OTrz7KuM2nC8nmx8UzbblKvAQAj2+9XNu3mRS8AcPWC5zbP2zfvOiRJ2hJ9++23AEyfPh2AAw44AIAGDRqUrCZJUnGNfW9ybPyEFt2rXCcsM+rtSbE2RrTuE1vu0RXzY+PJ+UHjutFz5vx2/QG45aMXy+ZdvfA/AJy3eV6w8KtPY+N77tiqyrolScXz3XffAdC40Q5F39b1V/wMgMuuvQmA52dE3ykeclB23+WtXZ//d4+ZnDviJADuHP8IAMtmTymb17TxjkVpu7rtbkkuH30OANfdehcAHy1ZCkCHtrukXT7M31IN3H/v2Phzm/v4Q48/GZvep0fXrNus6pi9+8GHsfFwzNOxT6aXPC7l+2G2xz20kXT0YYOA1DUw6ZkpsSHAry8ZFVtnz+57xMYfnhj/N5+D+vdLu60t9fxW99kA+T8fqrq+IPtrrJjHv5htJ+/TYX/32H3XtMsX4z5dk31gS71OgvD8CMMfHDsEgPcXfgTAUaedB6TuMV8tfK2mS4wJx/W2634FwKjLrwFg6BGHAHDG6Mtj8zOdh+R5g9px7sK2jz18cGz48uuzAbj/kejf3X9wzpjYeunOS6nPFUCPrp1j48tXrgIyH+MPP15SYVpl99NshGMV+nA25zl5/Wd6JhdSeHfauHFjwdr86quvAHjrrbcAmDVrFgBz5syJjc+eHfWvpUvj9+OWLVsC0KdP9B3ewIEDATj//PPLlgnzevbsCcAOOxTvHbB3794AbNiwITa9bt26ALRv3x6Am26K7v3Dhg3Le1vf//73Adi0aRMAb7zxBgD77uvvjCg3oe9Aqj+F/lUdJnJLkqT/x96dx0lR3/kffxFARC45BgUEETk9EBWNiBIxIBolRqJGQdGHkZ/H6moW5ac/s7qaZFWiG41ZJdE1RgNJPCAG0RiMB4q43jd4IaKgMiCHoCgqvz+K7/RUTfd090z39PTwev7TXde3PvWtb1dXzcC8JUmSJEmSJEmSJEmSJEmSJEmSJEkFZiK3JEmSJEmSpCbviTXRXwy/r/INAG7/8MXY8vN7DQPgqIoBAOzWJnsC1OsbVgAwb3XUdkj/DUZ37gvAuK5RunGmJOTu86bGppePmFL1fu6qtwE45bWZsTZP2nFwbDq4tzJKMQ5JysFNg8bWWkNtdWRrc2THPkAqGbkQMp2vcLyTeuwLwEHbp/8L8Pm0GdRlDGST7NP6qD4u8hH6LIyl+rSRTejj8Fl4aN9Tq5blu/8/7D4ur/ULOf4kSSp39913H5BKLWjbNkqoOO644wAYP3581bojR44EUskHUrmbv3YpALNXvgnAHR+9FFt+Xs8oof6ozlFa0W5tKmpt7/UNlQA8viaVuhWShYPRnaKEmmMqonTjTEnIPeZfG5teNnwyAHM/eQeAUxf+tUabE3bYMzYd3LsySkgOScrBjQOOzKuG6nVka/PQjrsAqdTlQsh0vsLxTuoepRgO79Cr3m0G+Y6BXKTr17oI56IuQp+F8VSfNpJuG/SDvNop5BiRJDVdIaUwJG6G1Mi/z/gtAHvvGf0sO5m4F9JBZ8yM37MA3H7DlTntOyT1JdsOSZYh4fG6391etezsU08AoKJzp9g2ISk01B+SRM+fdHJObWdqN5+2y8V3hkUpeCHp9dY/Rb/3OO3E6GfBIbUxJLyG5eUqjK8wLkPSaFBbWnYm2fos+bkIfZ5OvmOyqY3HTJL9Un0c5trvh488KG3bA/ruEt9XIpUVYOedute6TXIcJZdXtZ3l/ELjPMd1/W6AzN8PuX43ZPt8pWs702esrp8vKPz3SfK81tZ28jodzsN1V1wENMx1uiHHQLl9N597yX8CqXrnzfoDAPvvHf3sqL7px7nIdA+Vj4MP2Dc23XPfUbHp0d85sNbtk+cNGvd3WDg/4XXSSVFC9MNP/G/R910fAxPfL+FzlekaOev+h2q0MXTIHvWq4UdHHwGkErkffHQ+AMePHZNxm7BOEMZLKYUk3yVLlgA107TDa/Vlb78d/duKr776CoBtt90WSKVnhzTtMWPGxKbD6w477FCEI6m7UFfS1VdfDcC5554LwDbbbFPvfXXqFH3+R42Kri2PPvooYCK38vfII6l/AxjGUxhf9WEityRJkiRJkiRJkiRJkiRJkiRJkiRJkiQVmInckiRJkiRJkpqsZKJ1JtctXRB7vXPwj4Caic/VU4WztRnWTSYR15aKnW0fyTZD4nFImQ71JyXTtLPVUH0fyW2TbYa05nwTlNOZuuRxIPNxJI8/pGhP6X1wxjYLPQbKTUhvD/0QEtZrGwNhnWQbmSz+/BMAjn/5L0Aqrb0Qqea5CjVUF+qQJGlr89lnn8Wm16+PklL++Mc/AvD73/++alnnzp0BOOmkkwA48cQTAfj2t79d9DqlQkqXap3O9e8/FXu9c48oqT6Z+Jxre9XXTSYhZ0rFzmUfyTbnDpkIwH2r3ozVn5RM085WQ/V9JLdNthnSmvNNZ05n6tIoHSfTcSSPP6RoT+k1PGObhR4D5Sakt4d+CAnrtY2BsE6yjfpa/PnqGvNCsnvSq+tXxKY7tmgNwPSPowSiKW/PjS2f2jdKUvp+lwGA6d+SVM767RL93DWZVlw9MTRXIdk4UzpfSPcMaX477BlNT5oQJSLe8Iv/B8AhB+4Xay8kkibf17aPCePSf+dlajtbu7m0XS7q0wflbMwh6e9hjzg08+81sul34PdqXR76OPR5OnU9H/mMx2177xOb3rjk+azblFp9rgNhm9BHSSHBNtl29XT2ZMptmA7Xq5BAG7bJlIpbrtechvxuyFhDls9X9bYzfcaK2f/1/ezWpe3w/Zl045U/rXWfddGQY6DcPicnHxv93jNcB0Ycc0pO29XnPOV6D5WPcI6T17UwnS1ZPHneoDjfYcUyeFD/2GtjFeoLfZZrH4fzWL2NTLLdJ4R7qFBDuB6E19rkci9UaCFF+4wzoutVSNp+9dVXAfj0008BaNasGQC77BKlnu+5Z+rncccee2xsXkiy7tevHwDNmzcv3gEU0a67Rj9Xnj07+jdGBxwQ/ay3S5cuRdtnSPkOfTpp0iQAtt9++6LtU03DmjVrAHj44Yer5t19990Fa99EbkmSJEmSJEmSJEmSJEmSJEmSJEmSJEkqMBO5JUmSJEmSJDVZyRTmZ759JgA9WrWPzX9u3XIAxr4YpRSGhOtkGnO6VOfZQ6L0wn3bd4/NX/bFOgD2+99pQCrBOlsa9gufflj1ftGB5wHQvkWUMPXEmveAVPLxqOduA1LJ1NnWz7UGgD9+FKVfJfssHNf0D18CUgnWYV91SbAO24a2wvGcudP+seNZ99UXAEz74OnY+kdVDKhqK5kCXegxkI/lI6bUedtCCYnpIWH85mXPAZmT1tNtk6kPwvm4fPGjQOq85TK+Cu3uj1+reh/qH9mxT4PXIUlSY7Zp06Ya81atWgXATTfdBMD1118PQM+ePQGYODFKAR4/fjwAu+22W9HrlOoimcL89NAoYSN53//8luetsS/PAGD2yijhOpnGnGxv9uDxVe/3adcttiw8I+3/7M1AKsE6Wxr2C+s/AmDRAecA8WTh+WuXAnD8q3cBMPrF24FUMnVym+T6udYAMP3jKB0m2WdVz35blocE67CvuiRYh21DW+F4zuoxNHY8n34dPWvctOzZ2PpHdY6SfHZrU1Gj7UKPgXwsGz65ztsWSkhMDwnjNy+PUowyJa2n26ZQqeR3V74eax/g0I675LRtGOuZhITuuZ8sBuCG/kdULTOdW5LKU0jJHLbvXgDMfexJAOb8M0p+TKaAhnS97wxLpetlS9q7bPLZAHTfIfrZcUh+XP7xilrXH9Q/9V32+FPPxbYNQsrl2MMOAaCic6ecagltZ2q3Lm2Xi2Qf/OXeB4DUuQ7nePyWtMw9Rx7T0CUWVKbE5F169ci7rdB3Hdq3A+CiX/wKSCVVnnvaBCC/9Mlcx2RTHY+ZpLsOJMdq6PcfHR3dk+aa/BzS2EOqafXrWSaHjzwISJ2XXBPdM53f6m0Fjekc5/vdADW/H/JNYc32+YL8P2P5fr6g8N8nyfNal7aTYz+kZYfzdPbFP6+1vbpoyDFQLt/N++8dJfQ+/cCfAZh1/0NAzYTk0A9D99oDiI/hfOV7D5WPHx45OtZmSBzPtzbwO6yYpl19KQCz//EoUPMzGMbXkd+NXn941GEF23e4h7r1V9E15sFH5wM1r0mQSgIP46ohk7iDd999F4DWrVsDsM8+UeL4qaeeCqRStvfYI/pstmvXroErLL2jjjqqwfZ15JHRs8SwYdG/YbnlluhaecEFFzRYDSpPYayEsQOp8VQIJnJLkiRJkiRJkiRJkiRJkiRJkiRJkiRJUoE127x58+ZSFyFJkiRJkqS6mzEjShCaMCH6C9CNIQFWaixCGvPcVW8DcGmfkQDs0Tb6q9GD2+4IpBKf62Llps8AWPHlegCWbYxSy0KydkiNDpKf0e7zpsamXx52TtX7Li23S7vP5DbJJO5s66e7TiTXeWK/6K9192md/i9SJxPHJ3YbAsBV/eJ/ZTnZbrr9T13yOJDqq2zHE5KgBz4ZJUaG8wpw5k7xv67cEGOgHEz74BkArlj8SM7b/LJf9NfuJ3TbK+3y5HkLYzfduM1lDNZFsgaAh/Y9FaiZzi4p8i+L7qt633pUdO2ePn16qcqRVATJZ8T6aNmyJZBK8x40aBAAp5xyCgAnnngiABdffDEAn899AYDf9P9evfct5SOkMc/95B0ALu39HaD6ff8OQN0Te8NzH8CKLzcAqWeikKwdUqODZEpzj/nXxqZf2v8sIPNzX7pt0qV317Z+thoAHt/nNAD6tO6Yts1k4vjJO0bPB1ftOiqvfQNMXRol54S+ynY8IZl74FO/AVLn9YwtCd7VFXsMlIvfbkkxv2LJYzlvM7VvlFY0YYfB9dp38vzOHTKxalm6FHWoOW5mDx4PUCP5Prh35SIglTR+44BUGkouCfRSoc2qXAjAOW/eD4D/FFPlLjxDfLNhNQC3Xf+LUpYjAbBt7yjNMCQt3vCL/1fKcvL21rvvAalk8XyOIxx7sHHJ8wWuTtp6+fmSJBXCqeddAsC32kQ/W/V3ro3Lq6++CqQS0n/9618Dqd91ScHChdHP+P71X/8VgOefT90b7rHHHgXbj4nckiRJkiRJkiRJkiRJkiRJkiRJkiRJklRgLUpdgCRJkiRJkiQVy//tfRCQSmNOphGP7twXgEk99gXgoO13zrntdEnAhVBbGlsmxUiTzpTEHfRo1T42ffuHLwI1E7lzkezDkLSdq+rnNZnIXcwxkE26NPK6qmuC9b1bkqHCcd80aCwAR1dk/gvDYZuzFs4GoG2LbWLbhOXhvM0echJQt7FbV8nPX0jhBpO4pXw888wzABx//PElrkRSIS1durRgbYUk7mDRoigJ9ZJLopSJiy66KLb8iM79CrZvKR9Teg0HUmnMyTTi0Z12BWBS9yh5Y3iHXjm1m0wYLqS63D8XI006UxJ3kHz2u+Ojl4Caidy5SPZjSNrOVTiv6RK5izUGcpEu6bwu0qWY5yokVYfjDknVtaVUJ9Ot2zbfJus26WRK4s6Uwl1dvsccags1z6pcVGOZJElqnJLpt/Nm/QGA/ffeMzZ/7afrAfj9n2fF5h98wL5FrK54ZsycE5uedNKxJapEkiRJ2rqEJOWzzz4bgP/4j/8A4De/iX4uXVGR/eeXatoqKyuB1NgIY6WQKdzVmcgtSZIkSZIkSZIkSZIkSZIkSZIkSZIkSQVmIrckSZIkSZKkJisk84Y049c3rABg3ur3gFRKcUhrDunMIcU5mew7/cOXqt6HJOCJ3YYAcFTFAAA6tWwNQNdt2gIweEF+CWMqrEKPgXITUrWD2pK4k+uEbWeuWJh2fjD2xT/mXVcyrTxb4vjKTZ8BcOuy5wB4bUP0V3Gf2O90IHuCvCRJahjNaFbqErSVCsm/Id339S33i4+v2XLfvyWlOKQ1h3TmkOKcTA6e/vHLQCph+OQd96paNrZLfwA6tgjPfm0A2Ovpmwp2PMpfocdAuQkJ1UEu6dSZ0q2zbVv1fPbhC0C1vt7nNCB7ynshhfMpSZIav3tuuQ6AH55+PgAjjjklp+2OHDUCgDGHDC9OYQWWTB4PLj43+ln24EH9G7IcSZIkaat31VVXAfDkk08CqfTla6+9FoBtt922JHWpdDZu3AikxkKvXr2A1FgpFhO5JUmSJEmSJEmSJEmSJEmSJEmSJEmSJKnATOSWJEmSJEmStNUI6crhdeyWFO13P18NwPEv/wVIpTMnU4IvfOvBGm1e1e+wtPta99UXBai4dJZ9sQ6AHq3ap12++PNPYtPn9xpW532FVPPbP3wRgEUHngdA+xat6txmJvUdA/moz7aNSeiLUggJ6lcveQKA3bek5F3b/3AAurTcrjSFSU3EfvvtB8D06dNLXImkQpoxYwYAEyZMqHdbLVu2BGDTpk0ADBwYpbSeckqUnHbiiScCcPHFFwPw+dwX6r1PqRBCunJ4PWpLivaSjWsAOP7Vu4BUmm9IcQ6mvD03Nn3VrqMy7uvTr5v6s9/q2PR5PQ+o875CsvkdH70EwKIDzgGgXfNiPPvVbwzkoz7bNibZ0q1D8vbUpfOBVN9e0zf6uUhdns9OXfjX2L6zjYnk5y2MKUmS1PiFZO2/z/gtAI8teAaAK2+4JbbepAnHAnDwAfsCqSTuDu3aNkid9RWOc85D8wC46pKfAHD+pJNLVpMkSZK0NQuJ23PmzAFSvyO/6KKLALjiiisAaN8+/c/I1XSsWxf9PuTSSy8FYP369QA88sgjQPHT2U3kliRJkiRJkiRJkiRJkiRJkiRJkiRJkqQCM5FbkiRJkiRJUpN10Vv/AFJJz7OHnATAvu27A5kTx/IRkqn7tO4EpJK4p33wdL3bLqXpH0YJaRO6RelWoa9CWtvdH78WW//A7XvVeV9HbUnFDucp9N1pPaLEjWSq1xNr3gNS6dmX9hlZtezMnfaLrdsQY6AxC31zxeLor8eGvhvcdseqdZLJ5/dWLkzbRlCXpPHu86bm1UYYZ6Oeuw1IJb5P6X1w3vuWJEnZbbPNNgB8+eWXAPTs2ROAiRMnAjB+/HgAdttttxJUJ2V30TsPAamk59mDozG7T7tuQP3v+6unUvdp3RFIJQPftOzZerVdatM/fgWACTvsCaR59qt8Pbb+8A4967yvsVtSscN5Cn13Wre9gZrPfvPXLgVS6dmX9v4OAGf0GFqj7WKPgcYu9M0VSx4DUn03uO0OQPqE63tXLkrbRlIYC6NfvB1IpbJP6TW8vmVzTMVAIJXI/fDqdwE4usvAtOuH5UEYU5IkqXwccuB+sdfLJp9dynIK7p5britYWxuXPF+wtiTF+fmSJGnrU1FRAcADDzwAwJFHHgnAv/zLvwBw5ZVXArDTTjuVoDoV0wcffADAxRdfDECLFtF/qQ5jIYyNYjORW5IkSZIkSZIkSZIkSZIkSZIkSZIkSZIKzERuSZIkSZIkSU3WcTvsAaTSmMe++MectvtlvzFp5980aGzV+7MWzgbgoGduyaumZIJ3Y7ff/06rdXlISj5o+53rvI+wbWjruqULYq+ZjO7cF4Bjd9g94zqFHgPlJvTNgrXvA6kU81zk0r/F8ugn8ZS1XMdEOnVJEJckqSlq2bIlAJs2baqa17lzZwBOOukkAE488UQAvv3tbzdwdVL9HN81umcNacxjX56R03ZT+45OO//GAVESx9lvzAHg4OdvzbumkOIdErwbu/2fvbnW5SGFeXiHXnXeR9g2tHX9+0/FXjMZ3WlXAH7YdbeM6xR6DJSb0DcL1kXJIiHFPBfZ+vfRNUti07met3SWDZ8cmz604y6xGsJnLrxmUojxKEmSJEmSJGnrM2jQIACefvppAH7wgx8AcOaZZwLw4x//GIDvf//7Vds0b968IUtUPX399dcA/O1vfwPgf/7nfwDYa6+9APjrX/8KQJcuXRq0LhO5JUmSJEmSJEmSJEmSJEmSJEmSJEmSJKnATOSWJEmSJEmS1GTt2747AA/teyoA91W+AdRM9Q1J0Hu36wakkoiTjq4YVPV+/VdfAnDhWw+mbSukGG/85isARj13GwAL1kTJyI09kXtK74MBaN9iWwCuWPwIkOqbST32BeqXxJ1pnwPaRH/xNPRVSNMOQlr2mC79AOjScruMbRZ6DJSb0Dc3bEkUfGT1YgBmrlhYtc7cVW8DqWMe1zUa5yM79gGgfYtWDVNsNcnPlSRJyt1228Xvjdq2bQvAcccdB8D48eOrlo0cORIwSUDlb58t9/Fzh0wE4L5VbwI1E4NDiu/ebXcEUinASUd3GQjA+q+j574pb8+tsU5o69iKKMU4PPuNfvF2ABasC89+jTuRe0qv4QB0aB7d91+x5DEg1TeTuu8DFDb5OOxzwHadAViwNkqRDmnaQUjLHtMpelap7dmv0GOg3FQ9+/U/AoCHV78LwKzKRQDM/eQdIH68x1RE4zykYrdrnv7ZL934L5Swz2x1n7xjlJQytkt/wCRuSZIkbR227b1PbHrjkueb5D4bwtpP1wNwz33/AGDOP+dFrw9Fr0eOGgHAj46Onk3GHLLlWbld23rvO9mn9ZHpfITje/DR+QD85d4HgJrHB3Dkd6P3Yw87BICKzo37d9aSJBVLSGP+5z//CcBll10GwH/9138BqTRngLPOOguA/fbbD4BmzZo1WJ3KbvPmzUAqZX3atGkAfPBB9LuHf/u3fwPg8ssvB6BVq4b/d1BgIrckSZIkSZIkSZIkSZIkSZIkSZIkSZIkFVyzzeG/nEuSJEmSJKkszZgxA4AJEyYAsHzElFKWI6lMdZ83NTbttUSSmqZ/WXRf1fvWo4YAMH369FKVI6kIvvoqSgV+4IEoeeewww4DivvX5cPz6OdzXwDgN/2/V7R9Saq7HvOvrTFv2fDJJahEkpqGWZULATjnzfuBVPqPVK7Cff03G1YDcNv1vyhlOZK01TORu3DOveQ/Abh5+t05rR8SrO+55bp677sQidyZ6glJ3Kf95KdAKoE7nzanXX0pYDK3JNXFqeddAsC32nQE/J1rU/H2228DqRRngNmzZwPQs2dPAEaMiL5H9957bwB69+4NQPv27QHYZpttGqTWrcGXX34JwLp16wBYsmRJ1bIXXoh+LzlvXnQP9P777wMwduxYIJWu3rdv3wapNRsTuSVJkiRJkiRJkiRJkiRJkiRJkiRJkiSpwPyP3JIkSZIkSZIkSZIkSZIkSZIkSZIkSZJUYC1KXYAkSZIkSZIkSZIkSSqMFi2ifwYwduzYElciSZIkSZKkutq45PmtYp/F9PLCNwG4efrdAFx87ukAnHbiOAB6dt8RgPeXfwTA1P++Nbb+W+++V9VWv112rlMNdenTUPf+R5wAwFWX/CTteg8+Oh+AOQ/NA+DGK38KwA+POgyADu3aArD20/VV21z3u9sBuPKGWwCYPnMOAOdPOjnvOiVJaor69u0LwN/+9reqeQsXLgTgtttuA+C+++4DYPr06Q1bnGJ22203AE488UQATj31VAAGDRpUqpJqZSK3JEmSJEmSJEmSJEmSJEmSJEmSJEmSJBVYvRO5r776alavXs1VV13FwoULueSSS9iwYQNDhw7l8ssvp0WLFixfvpwLL7yQVatWscsuu3DNNdfQpk2bQtQvSZIkSZIagWbNmhW8zc2bNxe8TUmSJEmSJEmSJEmSJDV9z774amx6/LgjgVQSdxCmJ510LJBK5H7h1UVV69Q1kTsflas+AVJJ3CFhO9O+/3LvA7HpkDSeFJK5Ac7/PxOBVCL3Rb/4VTTfRG5JkjIKCc9XX3117HXNmjUAvPbaawCsWrUKgC+++KKhS2yyWrVqBUDnzp0B2H333auWbb/99iWpqa7qlci9YMECZs2aVTV94YUXcumll/Lggw+yefNm7rzzTgAuv/xyxo8fz9///nf22GMPbrzxxvpVLUmSJEmSJEmSJEmSJEmSJEmSJEmSJEmNWJ0TudesWcOvfvUrzjzzTBYtWsSyZcvYuHEjQ4YMAWDcuHH8+te/5rjjjuOZZ57hv//7v6vmn3TSSVx44YWFOQJJkiRJkiRJkiRJkiRJkiRJkqQSunP2g0AqKXnOQ/MAuPjc04FUIvSeI4+pse3GJc/HprftvU9ey99/7iEAps+cA6SSlo8cNQKAHx19RNW6x48dk7b+bPvMR7KtuqpPDe8v/yg23bVL51rX79a1S2x64Zvv1HnfdXHjbX8GUucsU8J2cM8t1+W9j+rp3JIkqX5CIvTw4cNLXInKQZ0TuS+99FJ+8pOf0L59ewBWrFhBRUVF1fKKigo+/vhjVq9eTdu2bWnRokVsviRJkiRJkiRJkiRJkiRJkiRJkiRJkiQ1VXVK5L7rrrvo1q0bw4YNY+bMmQAE/rIhAAAgAElEQVR88803NGvWrGqdzZs306xZs6rX6pLTkiRJkiSpvG3evDnrOsmfB+SyjSSp4SwfMaXUJUiSJEmSimjZ8MmlLkGSJEmSmqTLr70RgCtvuCXt8jA/0/JCOPP/XgGkUsCDMJ2cD5mTuZuKZH9nS6Ou6Nwp4/aXTT67cIVV8+iTz9TY399n/LYo+wJ46933YtO333Bl0fYlSZKklDr9R+7777+fyspKjj76aNauXctnn31Gs2bNqKysrFpn5cqVdO3alU6dOvHpp5/y9ddf07x5cyorK+natWvBDkCSJEmSJEmSJEmSJEmSJEmSJEmSJEmSGps6/Ufu3//+91XvZ86cydNPP82VV17JUUcdxXPPPce+++7Lvffey4gRI2jZsiVDhw7l/vvvZ+zYsfz1r39lxIgRBTsASZIkSZIkSZIkSZIkSZKkUho4cCAAXbp0AaBz586x6fBaUVGRdr1088K6HTt2LGrtkiSpbkKackhSvvjc0wE47cRxAPTsviMA7y//CICp/30rADdPv7vgtQwe1B+AW3/1cyCVPh1qPHz8GVXr/uXeB4DiJnJvXPJ80dpuSm64dXrV+yNHRf/P5pAD9yva/mbMnBPb15hDhhdtX5IkSUr5ViEbu+aaa7jyyis5/PDD+eyzz5g4cSIAl112GXfeeSff+973ePbZZzn//PMLuVtJkiRJkiRJkiRJkiRJkiRJkiRJkiRJalTqlMhd3bhx4xg3LvqLUQMHDuTuu2v+dagePXpwxx131HdXkiRJkiRpK9asWbPY9HvvvQfAOeecA8Bee+1VtexnP/tZ2m02b96c1z6yrQ/w8MMPA3DXXXcBMG3aNADGjh0LUPUH7Q499NCsbam8dJ83NTa9fMSUElWy9Un2fTrJ87Huqy8AeGT1YgBmrlgIwNxVbwMwunNfAA7rtCsAY7r0A6BLy+0KUHF+Qq0AsysXAfCPT94BatY7rusgAEZ27ANA+xatGqzObEKtp7w2s2peY/ichP4t576Fmv3bmPsWyqt/69O3uVyfctWQ5zTXa2S+56369awhr7/5nsNs560xfL4kSeWjx/xrY9PLhk8uUSVbp2T/JyXPx6dfR/crD69+F4BZW+5l5265lx295R5ldKc+VduM6RTdv5TieTHU+7eVbwAw95PFW17j9R5TESXCHtpxFwDaNW8899uQqvfUhX8FGsfnJFvfQnn0b2PsWyivsZusdcrbc2usc17PAwA4tmI3APq0Ll1Scq7XsWTfQt37txDjrK51J2vOdt2va311NWHCBABWrlwJwKpVq6Iali0D4KWXXgKgsrIytnzDhg1Z227RIvqnnsmU7+R0Mu0703rV53Xt2jW2rG3btjkcrRrKddddB8Dvf/97IApcAhg1ahRQ83dbkqSG9diCZ2LTySTuIEz/6+nR/UIxErnPPvUEIJXEHaRLd57z0LyC71/5efqFV4D4ubjnluuKsq/Lr72x6n1Ij3/6gT8DNceLJEmSiqOgidySJEmSJEmSJEmSJEmSJEmSJEmSJEmSpAIkckuSJEmSJJXCzTffDMDs2bMBGD9+fIPu/9///d8B+PnPf552eagrvP70pz8FUmnhkhpGSIM99405QCqlNSnMD68hSfja/ocDDZu09p/vPlb1/vYPX0y7TrLekGj7h93HFbm67F7fsAKIJ3E3JqF/y7FvoXH3b7a+hcbdv42hb0N/NISVmz6rej/5zb8DuV8jQ52ZrpHJa28+bdfn+tsYzqEkSSoPIQn23DcfAOKpy9WF+dWXz+0UJQlf0/cwoGGfF3+x5HEA7vjopbTLk/WGRNvbBv2gAarL7vUNUQJtSPFtTLL1LTTu/m3MfQvlNXazXRcArn//qdjr3CETAditTUWRq0sJz3QXvP2PqIYcr2OhbyH/61ghxll96y7FtTcf4fcG+fr888+r3oeU7pDqHdK7w3Qy7Ts5/dZbbwGwYMGCtOt98cUXWetp1SpKPs+U5h0SvLOlg1d/n0wKD+u2bt06az1buz/96U8AvPzyywAcdlj0ORgwYAAAF1xwAQAnnXQSANtuu21DlyhJW7WQbhwkk7iT+u2yc9FqqejcqWht18W2vfcpSDsblzxfkHYamzvunl1j3kHfLkyfBSGJu/o4DUncgwf1L+i+JEmSVDsTuSVJkiRJkiRJkiRJkiRJkiRJkiRJkiSpwEzkliRJkiRJZWn33XcHYPPmzQ22z4cffrjqfUjiDknbIfGgQ4cOAKxduxaAa665Jrb+scceC8Bee+3VABVLW4flI6ZkXPbI6igpLSS9/rLfGADGVgwEoH2LKF0mpMdO++BpAK5bGiXW3P3xawCcudN+hS67hpAkWz3N+PxewwCY0C26ZvRo1R6AZV+sA+CGpU/Ftln8+ScA9Gnd8H9x/7l1ywEY++IfG3zfuUj2b137FuzfpGx9C1vP2K3tepRO6LtRz90GwGV9Dql3Dbl6cOVbVe/DNfKmQWMBOLpiUNpt7q1cCMBZC2fH2qh+rqHmtReKd/0N5w/qfg6T5637vKl1akeSJDU+y4ZPTjv/4dXvAqmk16l9RwPw/S5RsmW75tG9SkjuvmnZs1XbhgTee1a8DsAZPYYWuuwaQgpuSDM+r+cBAEzYYU8gzf32lnursP7iz1cD0Kd1x6LXms7zn34IwNiXZ5Rk/7XJtW+hcfZvOfQtlMfYvXflIqDmdWHCDoNrrDt/7VIAjn/1LgBu31LvVbuOKnqdwYOfRM9bod4bBxwJwNFdBqZdPxzf2W/MqdFGumOsrpDjrL51J2tOd53vMf/aetfZ0KqnUu+0006x10Jbv3591fuQ0r1iRfTziUwp38k08JAS/sorr9S6HcBXX31Vaz1t2rQBUgndIbk7vOaS+p1M+860bsuWLWutpbHatGlT2vkhff2MM84A4MILLwTg3HPPBeDss88GYMcda0+GlSSpKbv43NOBVCL12k+je6EO7dqmXT8sT25fSJWrot+J3Tz97hr7yFRXvm3feFuUuv3ywjcBeOWRWVXrFDMVXpIkSZmZyC1JkiRJkiRJkiRJkiRJkiRJkiRJkiRJBWYityRJkiRJKkuHHnpog+/zkUceqTEvmcQdhOmwPCRyP/TQQ4CJ3FJDmbliYWw6mRobhGTYM3faH0glwl6x+JEt84ufyP3Cug9rzDt2h92BeAJY9emJ3YcAqVTjV9Z/DDRsqvG0D54BUn0VEn1DYm9jkezfuvYt2L9JufZt9Xlb+9hduekzIJXEHdKqG/L4L3zrwRrzMiVxJ5eHPgptJK+tyWtvunWCul5/k+cPGufnQ5IkNU6zKhfFpjOl0YZk7rOqpW6HRO4rljwGNEwi9wvrE/fcFbsBtTzP7Bjde4VU41c2hPvthkuM/m21FPPQVyF5t3oicKnl2rfV5zWm/i2nvoXGPXaT14XvdxmQcd3hHXrFpu8oQSL3lLfnxqYzJVonl1cfI6GNTNfAYoyz+tadrWZl17Zt2xrve/fuXbT9rV69GkileGdL/Q7rJVPAlyxZkna76vOyad8+utZ07doVyJzcnZwO6+eybpj+1rcKl7GU6fi++eab2PSaNWsAuOqqq2KvJ5xwAgA/+clPqtbde++9C1afJG3tkonP7y//CICe3XdMu35YvjXYuOT5UpfAoP67xqZXrIzuITIlX7/3wfLYdKbzWB/vLl0Wmx661x71bjMkb19+7Y0ADB7UH4BpV18KQEXnhvv9kyRJktIzkVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmSCsxEbkmSJEmSVJaqJyA0lJCqXd3222+fVxshoXvy5MkFqakp6j5vKgATu0VprVf1OyznbS966x9AKuF10YHnAam0z9c3rABg3ur3gHiKJ8Dozn0BGNc1ShzNlkxam3AcwfIRU/JaP5dtnlgTHcd9lW8AqeMOxzGpx74AHLT9zjlUnJt0ddZVtuMrhD/sPi6v9cNYKYVlX6yrMa9Lyza1btN1m/hfi39jw5Z0loqClZVV+ByFvg7jr7El4ib7t859C/ZvQr59C47dW5c9F9tnprTqYgr7Bpi76u16t1FdvtdeyP/6mzx/1etpTJ8PSVLh9Jh/LQAnb0lrzSf19KJ3HgJSiamLDjgHSKUtv74hSn58fMszVkgeDUZ3SqU3HVMRJYNmSxDNJBxHsGx49p8P5LvN/LVLAZi9MkpjCscdjmNS932q1k2mytZVssb6yKVP6uu2QT/Ia/0wVkpl2RefxqYrttmu1vW7bhO/J3/js1UZ1iye6p+j0N9hDDam1Oh8+xYaV/+WU99C4x67+VwX5n7yTmw6JFU3pHDOk7XUpY1MijHO6lt3tprV+HTs2DH2WgwhmTpTyndyesWKFWnnh9dFixbFpkNKOMC6dTV/hltds2bNgNyTuysqKmKv6datnj6ei02bNsWm//znPwNwxx13VM076KCDgNTvrMaOHZvXPiRJKd8Zth+QSuS+9U8zATjtxOhn1yHROSRxh+VqGAP77hKbnjEzupfNdH5m3f9QbP2hQ+qflp306qK3YtP9d6377/JD3fsfcQKQSoi/bPLZdW5TkiRJxWEityRJkiRJkiRJkiRJkiRJkiRJkiRJkiQVmInckiRJkiRJalQu7TMSSCVtXtA7Sobo0jJ9Ys/KTZ9VvQ+J1KGNkO4ZkkZPea32v3Ae1ksmk9YnmbsYpi55HIDrli5Iuzx5HOf3GgbAlN4HN0B15W3x55/Epm8a1HBJJOnOZ7aE2uTnIrTRkOe6IZLVCyHZv3XtW7B/k/LtW9h6x+4TW1I+w/HeOfhHDV5DcNKOg6veh++LeysXApm/98LydG3UV77X33L4bEiSCuvS3t8BUumgF/Q6EMj8rAip58WQSB3aCOnKIQ301IV/rXXf1VNDkwmidU3mLoapS+cDcP37T6VdHmqvfgzn9TwAgCm9hhe5uvK2+PPVNeY1ZAJv8pxmSwhPfi7C9g15nhsiWb0Q8u1bsH9zle5aVA5jN+m3y56tel89aR5S14FSfBdM2GFPIHVNv3flolprCcvTtZFJMcZZfevOVrO2Tt/6VpRn1LVr19hrMXz55ZdA5tTvkN4dXjOtt2zZMgBefPHF2PLq7z/7LPW7j/pIJnQDLFgQ/WzqmGOOAaBXr15AKjm9707F60NJamoOOTBK5A5JyCGZO7yqtAYP6g/AkaNGALmfn0kTjo1tX5tte+8Tm9645Pla13/h1fg97vbt22XdRyZzH3syNl2f8ZetbkmSJNWPidySJEmSJEmSJEmSJEmSJEmSJEmSJEmSVGAmckuSJEmSpK3eihUrclrvzDPPrHo/bdo0ANasWQNAhw4dCl/YVmpEx51j0/O3pJdmSgcNy6s7rPOuselkEvfsIScBsG/77rH5y75YB8B+/xud37MWzq513w0tmeQakrbP3Gl/IJWCu+6rLwCY9sHTsfWPqhgAwG5t6p6m0dQTUO/++DUARnfuC8DIjn1KWY6kJubmZc8BqWvMQdvvXNvqRRVqgFQyeKgvfP9l2iasX8j6vf5KkrI5OPG9M3/tUqD2FNSwTjC6U/xZMZnEPXvweAD2adctNj88KwLs/+zNAJz9xpys+28o4ThDcm1I2T6rx1AglYD76dfRs+JN1dJlwzZHdY4SpnZrU1GnGsohHbg+7q58vep9GEeHdtylVOVIakB7tE39HO3S3t8BYMG6D4DUd0HQkN8J4Vp05x7HAXDz8ufT1pRpfYDhHXoVs8Sc6si37lLULFW3zTbbANCtW7fYazEsWbIEgF12Kfw9x9dffx2bfu+992Kvzb4q/T2uJJWbyyafDcCg/tH9y1/ufQCAOQ/NA1KJ3ePHHQnAniOPaegSt2rTrr4UgNn/eBSAOf+Mzks4PyGx+8jvRq8/POqwotVy8/S7Y9MVnTvVua2zL/55fcuRJElSAzGRW5IkSZIkSZIkSZIkSZIkSZIkSZIkSZIKzP/ILUmSJEmSJEmSJEmSJEmSJEmSJEmSJEkF1qLUBUiSJEmSJBXL2LFjAZg9ezYATz31FAAHHHAAAGvXrgXghhtuyKm94447rur9tGnTALjmmmsAOPfccwHo2rVrbJuHH34YgO9+97ux9SdPnpzPoWxVdmsT9eHozn0BmLliIQBHVwxKu35YDjCx2xAA+rTuFFtn+YgpsemVmz4D4PUNKwBYtnEdAC98+mG9ai+2J9csjU2fudP+ALRv0So2P0yH5dctXQDAvNXvAak+VsrUJY8Dqb56aN9TgZp9K0l18dy65QDMXfU2AH/YfVwpy6nh1fXR92GoL5Ow/LBOuwJw0PY713vfXn8lSbnarU0FAKO3fA/NqlwEwNFdBmbcJqxz8o57AdCndcfY8mXD48/mqWfFymj5F1ueFdd/VK/ai23+2vdj02f1GApAu+bx79MwHZYDXP9+9LOSx9eE58WKotVZjqYunQ+k+glg7pCJQM3+ldQ0De/Qq8b7M7ZcR6d//DIAZ78xB4AuLbersU2xVT3PffJOreuF5aM79ama15B1JtW17lLWLDW09evXF6SdFi2ifyb8zTffVM0L7/v16wfAEUccAcALL7wAQLfttyvIviVpa3T82DGx12wmTTg247KNS56vddtsy+uyfr5tlouKztG/HzjtxHGx10IoxnkoRVuSJEkqLhO5JUmSJEmSJEmSJEmSJEmSJEmSJEmSJKnATOSWJEmSJElN1vjx44FUIvewYcPSrhdSsrM59NBDq97/9Kc/BeDnP/957DWTkA5+8skn57QvwaQe+wJw/Mt/AWDx558AqbTtMF09PfTOwT+qtc1k6me5SdY98Mnr89r+isWPAHDmTvvVuYbu86bWedukZFJ6KWRKgjW1XFIh3fXxq7Hpb3foWaJKUu6tXFj1Pnw/3DQoul85umJQrductTC6t2rbYpta16+N119JUl1N6r4PAMe/ehcAiz9fXbUsJG6HeSHF8849jqu1zXSpy+UkWffAp36TdxtXLHkMSKXM5qvH/GvrtF06yaT0UkiOiZDCDaaWS0r5fpcBAEx5ey4ANy+P0vAaIjX63pWLgNT1+8YBRwJwdJeBta4f0sMB2jbfptZtiqG+dZeiZqlUKisrc1qvefPmADRr1gyAr776CoCuXaOfsRx++OEAjBo1qmqb8L5bt26xtiZMmADANxtWI0nKzba994lNz5v1BwD233vP2Py1n64H4Pd/nhWbf/AB+xaxOkmSJEmNhYnckiRJkiRJkiRJkiRJkiRJkiRJkiRJklRgJnJLkiRJkqQm64QTTohNz5gxA0gldP/ud78DYNKkSQBccMEFObf9s5/9DIDdd98dgMceixJEpk2bFlsv7OPoo48GUgkIym5w2x1j0wvWvA+kErlfWf9x1m2C6R++BKRSPyd2GwLAURVRak6nlq0B6LpN26idBfmnl6k8rNz0GQC3LnsOgNc2RKkmT+x3OpAaX6Vwfq9hQDx5fd1XXwDQvkWrtNuE5ck2VFOyf+3bwsm3b6uvk2yjqQnXnNs/fBFIHWdtfdNQQqp2ddmStcPysO3MFQtz2g4a9/VXklReBrfdITa9YN37Ve9DIvcrGz6udZtg+scvA6nU5ZN33AuAsV36A9CxRXhWbFO1zV5P31Tn2tV4Vd2rfPgCAK9vuVd5fJ/TgNTYKpXzeh4ApMbqp19H99Ptmqe/rwzLk9urpnz7tvo6yTYUV71fmurYTR7H3E/eabB9V0/WhuwJ1WF59e1mVS7KadtCqm/dpahZKpVVq1bFplu2bAnApk2bAGjbNvp9xsiRIwEYM2YMAN/97ncBGDjQz4kkNYR7brkOgB+efj4AI445Jaftjhw1AoAxhwwvTmGSJEmSGhUTuSVJkiRJkiRJkiRJkiRJkiRJkiRJkiSpwEzkliRJkiRJDWLz5s0l2z4kcycTuguxj2TbN91kMlehhLTSX/aLUiQufOtBAMZ06Qek0kDD8urbJIVtg6v6HZZ2vWRCbEMIqV+5CEniIdl10YHnAQ2b7Lp8xJQG21chvb5hBQBXL3kCgN3bVABwbf/DAejScrvSFFbNgDZdasxbuWkDkPkcf/DF2th0j1btC19YE5HsX/u2cPLtW9h6+ve9z9fEpvdu161ElRTH3FVv17o8XHuhcV9/JUnlJaSfTu07GoApb8+tWjamU18gldoZ1smU/Fp9W4Crdh2Vdr1kQmxDyfV5MSSJ3/HRSwAsOuAcoPZE4UJbNnxyg+2rkELy9tSl8wHYbcu9yjV9o58bNJZ7lQHbdY5NV34ZjY12rdOf4/c3rotN92jVrjiFNQH59i3Yv7lK9i007rF76sK/AqlU7VyupcnrdLgel4uGTBAvlHKsWaqrwYMHA9CnTx8AfvzjHwOpxO2hQ4cC0Lx58xJUJ0kKQrL232f8FoDHFjwDwJU33BJbb9KEYwE4+IB9gVQSd4d2bRukTkmSJEmlZSK3JEmSJEmSJEmSJEmSJEmSJEmSJEmSJBWYidySJEmSJElq1IZt3zM2PXjBb2LTh3TaJe82F3/+CQB9WncCUknc0z54ui4lpjW6c5QEF9JKn1u3HIB923eP7fPWZc/l3OZRFQOAVCJ3qPe0HtFfbk8mhT2x5j0Ajn/5LwBc2mckAGfutF8+h1LWln0RJTmNeu42AM7vNQyAKb0PLlVJGfVLk1R198evATChW5ToFFKLw3HdV/lGbP292zettN9CSvavfVs42foWtt7+XbQl4THYdbtOJaqkpvCdAHDF4keA1PfG4LY7AjVT1e+tXJixjeqS115o3NdfSVJ5Gta+Z415ez19U2z6kO1759Xm4s9XA9CndUcglcR907Jn61BheqM77QqkEkWf//RDAPZpl7ofCvu99cMXcmpzbJf+QCqRO9R7Wre9gZrPivPXLq16f/yrdwFwae/vAHBGj6G5HkpZC/cro1+8HYDzeh4AwJRew0tWU236tU7cc1e+DsCEHfYE0txvr3oztv7ebZvm/XYh5Nq3YP/mK9m30LjH7jEVA4HU9flvK6Pn1gk7DK6xbrhO37Pi9dj8cD1uCOG6fcWSx4DUtX1w2x2Amkni965clLGNhlTfuktRs1Qq/ftH15R33jGJXpLKwSEH7hd7vWzy2aUsR5IkSVIjYyK3JEmSJEmSJEmSJEmSJEmSJEmSJEmSJBWYidySJEmSJElq1EJq9sRuQ4BUGnWYrp6MlMlNg8YCcNbC2QAc9MwtedWQTPDOxbiug4BUIvfYF/+Ydr1MaabpHLT9zkAq1fS6pQtir5mEdPBjd9g95301FY9+8m5sOtc+S1o+YkqNed3nTc26Tj52a9MVSJ0vyL3e8HkIbSQVutZiK0a9yf4tVN9CefVvslYo/NjN53PW1MZu0ivrP45Nt2+xbZ3bKnRfVP9OWLD2fQCOf/kvOW2b7Xslee2Fwl5/JUmCVGr2yTvuVTUvJFKHedmeF28ccCQAZ78xB4CDn7817zqSKd7ZJBNfx748I+O6uaaODu/QC0ilSl///lOx19qEhPAfdt0tp301FY+uWRKbzqfPkpYNnxyb7jH/2lqX18VubSqA1PnKtd7wWQjbp1OMeoulMfUtZO/fcupbKHy91ful0GM3WSvUv96ju0TX51mVUQL0lLfnxl5rE66/4XqcVIyxEK7bC9Z9AMDxr96V03bhXFRvoyHVt+6t7ftKkiRJkiRJUtNgIrckSZIkSZIkSZIkSZIkSZIkSZIkSZIkFZiJ3JIkSZIkSSoLR1UMAFKJ3MftsEfO2x5dEaVjr//qSwAufOvB2PKQcB2SRTd+8xUAo567DYAFa6Kk0nwSucM+g5krFgKphO5f9hsDwIRuUcLQFYsfybntKb0PBmBAmy6x+kLfBGEfY7r0A6BLy+1y3kdTkTzX5eDa/odXvX9w5VsA/GNLWl8YPyEN97AtaURjt6T6KbvQv/Zt4WXqW9h6+zd5XW5M1+HqtdywJY30kdWLgZrfWeG8jesafbeN7NgHgPYtWqVtuxyvvZKk8jW2S/+q9yGR+/iuu+e0bUhfXf919KyYTF0NCavHVqTSP8Pz4ugXbwdgwbrwvJhbInfYZxCSX+dWu3ea2nc0ABN2GAzAFUsey6ntKb2GAzBgu85RbWujtNPQL8n2AcZ0ir7nG9N9SkPIJWG3Mbqm72EAPPhJdJ8295PFW16j8RMSa0d3iu7Xvt9lQEOXWLay9S3Yv/VRTmP3tkE/AODeldH1OVynIVVvSAwP30GZkriLKVy3b+h/BAAPr34XqPm9Evr2mC3P34d23KWqjXbN0z/TFVN96y5FzZIkSSqubXvvE5veuOT5ElWydUueh1x5viRJknJjIrckSZIkSZIkSZIkSZIkSZIkSZIkSZIkFVizzZs3by51EZIkSZIkSaq7GTNmADBhwgQAlo+YUspypKLrPm9qbLrUYz7UU+o6clFOtYL1FlP1z1E51VsOtRabfVF8je17ptD+ZdF9Ve9bjxoCwPTp00tVjqQmIjyPfj73BQB+0/97pSxHW7Ee86+NTS8bPrlElaRqKWUN+SinesupVrDeYqr+mS+nesuh1nLWkN8FsyoXAnDOm/cD4D/FVLkL9/XfbFgNwG3X/6KU5UiSthImcpfW+8s/AqDfgXX7eZ7nS9LW7NTzLgHgW206Av7OVVLtTOSWJEmSJEmSJEmSJEmSJEmSJEmSJEmSpAJrUeoCJEmSJEmSJKkcPbduOQC/7DemxJVkV061gvUWUznVCuVXbzHZF5IkSeXh+U8/BGBq39ElriQ35VRvOdUK1ltM5VQrlF+9kiRJkrZeV13yEwDOn3RyiSuRJElqWkzkliRJkiRJkiRJkiRJkiRJkiRJkiRJkqQCM5FbkiRJkiRJUlnrPm9qjXnLR0wp+n6fWbcMgDN32q/o+6qvcqoVrLeYyqlWKL96i8m+KJ503yOSJKlp6DH/2tj0suGTi77PcN92Ro+hRd9XIZRTveVUK1hvMZVTrRlCersAACAASURBVFB+9ZaT5HVekiRJUn7eWfJ+bHrI7gNLVIkkSVLTZiK3JEmSJEmSJEmSJEmSJEmSJEmSJEmSJBWYidySJEmSJEmSVAfllIpbTrWC9RZTOdUK5VdvMdkXkiRJ5aHcEnfLqd5yqhWst5jKqVYov3olSZK2No8++QwA98yZC8DN0++usc7F554OwDHfGwXA4EH9a23z5YVvAvDwE/8LwEW/+FVs+ZGjRgDwo6OPqJp3/Ngxadvatvc+semNS54HYM5D8wD44ennx9o87YRxsengztkPVr2feO7FsWW333BlnWoIbSbbq97mmEOGA9ChXdu0becrnC+oec7CMZ972gQADjkwt9+tFGMMZJPs0/oI50OSJEmNl4nckiRJkiRJkiRJkiRJkiRJkiRJkiRJklRgJnJLkiRJkiRJKivLR0wpdQmSpCbE7xVJkpqOZcMnl7oESVID8rovSZLqI5loXZsrb7gl9vr3Gb8FaiY+59pmWC+8VpcpFTvbPpJtPv3AnwGYdf9DsdrTSSZq51pDuiTuZJshJfueW66rtc1sLr/2RqD240j2QUjRvmzy2bWuX8gxUG5efG1RbLpTxw4A3PqnmQCcffHPY8tvvPKnAPzwqMOAwiWtS5IkNXUmckuSJEmSJEmSJEmSJEmSJEmSJEmSJElSgfkfuSVJkiRJkiRJkiRJkiRJkiRJkiRJkiSpwFqUugBJkiRJkiRJkiRJkiRJkiRJkqSG8sPTz49Nv/Xk/QD07L5jjXWffuEVAEYccwoA98yZC8AhB+5Xa5vzZv0BgP333jM2//3lHwHQ78DvVc2beO7FABw/dkytdT/70qsAfPzKPAA6tGsLwKNPPgPA4ePPiPZ5xAkAXHzu6bH1a9sm1xpu/fNMoGafheMCuPVP0TpX3nBLbF/JPssmbBfaCccDcP7/mRg7nrWfrgfgut/dHtvmmO+NAmDwoP6xtosxBnK1ccnzddqu2MK4yeTsi38OwJx/RuPp1l9F0+EcSJLUlD377LMAPPDAAwDMmxd9H776anR/tnr1agC++OKLElTXNLVq1QqAjh07ArDHHntULRsxYgQARxxxBABDhw5t4OryYyK3JEmSJEmSJEmSJEmSJEmSJEmSJEmSJBWYidySJEmSJEkF9PHHHwPw5JNPArDTTjsBsPPOOwPQtWvX0hQmNWHd502tMW/5iCklqESFtu6r6C/UPrJ6MQAL1rwPwO0fvhhb7/xew6reH7h9LwAO2n7nWttOjpv6jJlkWy8POweAwQt+A8DEbkMAuKrfYXXeR9JFb/0DSPXFM98+E4Aerdrn3Vayn2euWAjA3FVvAzC6c18ADuu0KwBjuvQDoEvL7WqtadGB51Uta9+iVU41DHzy+rTLQ1vZ2knXVqb+T3ftqE1oB2DY9j0BGNmxT851SZIkqX56zL82Nr1s+OQSVaJC+/Tr6B7+4dXvArBg7QcA3PHRS7H1zut5AADDO/Tc8tora9uFHDfJtl7a/ywA9nr6JgBO3nEvAK7adVSd95F00TsPAam+eHropKiWPJ/9kn0MMKtyEQBzP3kHgNFbnvlGd4qec8Z0ip4Fazz7JWpadED0DNyuefbnolDHwKd+k3Z5rm2laydT/yfPWzahnWEdop/rHtpxl6pluRyjJEmSyseRo6IEvzkPRUmKIWF5yO4DAdh7z0FV64ZE7Wwpysnllas+Af4/e3caJ0V19238YlMU2WVVEEFRUVBA2dyNqAhEg7gSjY8Ro9whoqjRiMknRgMxGvU2ASPG25i4G41BY4y4ERVEEXciKCIIyr67gfC8qDld0zXTMz0z3dMzcH3fVFd11al/nzrVM9XTUz94e85cABYtjhKrQ6p2ZYw6N0pMTiYgZ0qGTqZWZ7NNeSZcfQlQMrm6+Px5Zw4D4lTsyiZYvzj9tbT58Hqg5GsK82GdsO/nXnoVKJnInY8xUNtcef3NafOZUuSDh6Y8DcTp7U+/8DJQfoq7JEm1xdatWwG4//77U8vGjx8PxMnbHTpEn1EfeGD0WeLw4cMBaNIk+tx2hx12qJ5itwPffPMNAOvWrQNgwYIFqecmT54MwM9//nMgTuu+6qro95QzzzwTgDp16lRLreUxkVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmScsxEbkmSJEmSpBy6887ojs7jxo0r9flwt8VwV8YuXaKkm86do6SbkNxd/HGnTp3S5tu1awfUnDsFSlKurdj0BQBj5/4LiJOhM7ll4fQSj0OK9G37DAaqNzE5pJWFpPBQ0wW7HwxA551aVLrt+V9GyQ0h9TqkRFc0jS2kVgOM/uBJIHM/h+Vh+u+itLabup4AxK+3+y5t0rZbsWlj6nF5/T/vi5VlPv/p12sB6Fa/dZnrJfdbWl2VVTwJPjwO4yzZF5IkSZLKF679Lvvw30CcDJ3JrYtmpE1DgjTAbV0HAdWbmBx+/w9J4aGuC9r3BqDzTs0r3fb8L1cDcep1SImubBL36LlPAWX3cXguNW0xH4Ab9zoOKH7tl35dtvyb6Dg23qn8vp/3xaoyn1/0VZSq0q1RqzLXC/ssLllXZYU+D9Pi4yzZF5IkSardfjF2FBCnMSeTiUNaM8Do80YA2adJ//KmiUCcCJ1LrVpW7O9MpSVxV9Xee+5R7jrJtO7J9z4CwG3X/6xC+0r2YZvuR2RYM7NwbMeMPDtteT7HQHkaduqVk3againhFd02JG+HRO4HH38qbbkkSbXVrFmzAPif//kfAF577bXUc8cfH/2cu/jiiwHYe++9q7k6lWXevHkAPProowCcfXb0O9///u//AvCHP/wBgN69exegupiJ3JIkSZIkSZIkSZIkSZIkSZIkSZIkSZKUYyZyS5IkSZIk5VCPHj3KfP6bb74B4KOPPkqbNmjQAIC6deP77m3atAmALVu2pLUR1m3bNrqD9aJFi6patiTVKI8sfQ+IE6B/u3d0Z9vjd43uaJtMvgopbgB3LY7ukBtSsJ9fHSWIndRqvzxWXLohrfZJq2X6muj9uiqJ3O9sWJo2f2qbAyrVTugXKNnPQ1vtC8Qp2iG9+/ZPZwLx6wnH6cLdo+SBPXZqlraPJV+vTz0u7zW/tm5x2vyf9x8GwA/ei+6WO3vdZwB0a1R+wlrx/ZZWV8btjriizOeLj7OnV0R387183tNAnB4f6pYkSZJUvr8tex+IE6Bv2GsgAMe32AvIfO1312ezgTgBG+C51R8DcNKu++ax4tINadk1rZ7p68K1X+UTud/ZmH7td1rr/SvVTuiXZB8DfHfX6Jo1pJiH9O5Ji18H4tcTjtOPdjsYgE4N06+xPvsmugbL5vUmr/3u3u9kAM6d83cAZm8I135lJ3KHfRaXrCuTxYeOLfP5MM6eXhVdK1/x4TOp50J6fKhbkiRJtVuP/aLf5UMi8dtz5gLw3EuvAunpzCGxOSQ0hyTn0EZw1/3R3zVCivTIEcMBOGVw9Lt4i+ZNAWjXelcAOvQ+NmevRxWXjzGwvQn9IklSbfXb3/4WgCuvvBKIv//5xz/+MbWOCdw1Wzg+P/3pTwEYNiz67k5I4u7Tpw8AEyZMAODyyy+v7hIBE7klSZIkSZIkSZIkSZIkSZIkSZIkSZIkKedM5JYkSZIkScqhQw45pFLbhfTtiqxrErekbdW1859Pmx/R7sAy1y+e0nbh7tFdVENq9EVzpgCFSeQO6dEDW0ZpciG9OZl4XRHh9YQ2ezdpX6naHl02p8SyTP0c6kz2bThOIZF7z0T62rsblqUeH9ZsjzLrCW2F1xWmQei78sZCcr+l1VVZxcdZqGPx1+uAuE9mrVsCVP64SJIkSduTaxe8mDY/ok2PMtcPv5NfVJQMXTyRe9QHTwKFSeQO6dEDW3QB4gTnZOJ1RYTXE9rs1bhdpWp7bPl/0+bL6uNQZ7J/w3HKlMgdrsEObdqx3HpCW+F1hWkQ+q68sZC87iutrsoK4yzUsPjrOP079Mkb66Pk8MoeF0mSJNVMIVk5TEOKNsBHC6K/zZ9w1o+AOIU4JDkHo666Lm3+tut/Vuq+1q7fkIOKC2fRks8B6NC+bcZ15n38Sdr8VaPPr9S+Qqr55HsfAWDpO3ECdNPGu1SqzUxyMQayVdntcu2U88cA8esJ/Zupb5NjNxwfSZJqi/Ddy1GjRgHwf//3fwCMHj0agJNPPhmAOnXqFKA65UJI6L755psB+Pvf/w7AVVddBcDcuXMBmDhxIgANGjSolrpM5JYkSZIkSZIkSZIkSZIkSZIkSZIkSZKkHDORW5IkSZIkKYfato3uON2+fZTEuWTJkpzvo27d6N58PXpEqTCnnnoqAFdffXXO9yXl07rNXwPw/Or5QJwQ/MzKDwE4p91BAFywe5T41HmnFlXe5/sbo8SmaaujO8Ank59DCvCw1lF6czYpzi+tidp6YvkHANzz2Ztpz4/p2B+AIa2iJK6Q0lxee7lsMxvtp91Q5TYAlhxxRZXbCMc+vO4Vm74A0hORMwnp0bmoI1fCeApje94XK4GKpTaHpOdkm5X15/2HVXib8hLEd9uxSdr89LWLUo9DanfS/C9Xpc0fl0hj++3exwNxIndYv6z3g+L7La2uXBreZn8gTuR+bd1iwERuSZKk4tZ/G137Pbf6YyBOCH5m1UcAnN32QAAuaN8bgM47Na/yPt/fuByA/xRdXyWTn0MK8PdaRenN5aU4v7x2YerxlBVRUsNfPn8rbZ2LO/QDYEjLKLUrpDSX12am9irTZnl2e/mmKm1f3OJDx1a5jXDsw2vP9tovJEfnooZcCuMpjO15X0TXL9mmNoeU59LarKy79zu5wtuUlyBe4tpv3adAnNhdmvlfrk6bH9iic9r8DXtFKXchkTusn+n9IOyzrLpyZXirbqnHIZE7XPuZyC1JklS7jb7610Cc9DztsT8D0Kdnd6DstOlshVTqvffcA4jTjG+5454qt11Id93/KADnnRn9vSn0VUjqBrjv0SfTtjmyf+l/KypPSMUOx6l434069wwAWrVM/7vRC6+8BsTp2ROuvgSAMSPPTluvOsZATXf6SYOAOJH76RdeBuC0oceXun54PiieWi5JUk22detWAM44I/r94d///jcAEyZMAOCQQyr3u4pqrpCq/r3vfQ+A3XffHYBf/vKXAKxaFX2G/8gjj6Stny8mckuSJEmSJEmSJEmSJEmSJEmSJEmSJElSjpnILUmSJEmSVAXr168HYObMmQDMmBGlsjRvHqXFLF26FIBvv/220vto0KABADvuGKXh3HBDlJr7ox9Fd49+4IEHKt22VEijP4juwh5SipNCGnOYTu19LlC59Omwjx+892hW6yVrKi2ZO9s2Q1JvmD7U43QADmu2R6Xaq0ibtdU57dMTucfO/RcA32/bA4Buu0RjIJ9Jy7l0aOK4PLz0XaBiqc1hm0xtVodkevak/YaWul4yUR0yJ+u9s2Fp2nzPJu3KnP/oi9ITuUP7EJ9LoY58StYR0sAzJZBLkiRtj0bPfQqIU4qTQhpzmD5z0DlA5dKnwz7OnfP3rNZL1pRM5s62PYiTesP0oQNOBeDQph3z3mZtdU4ikfuyD6MElBFtouSzMAZqzbVf4rg8tOw9IPvU5rB+WW1Wh2R69sR9Bpe6XkUS1d/ZmLj226VdmfMffRmu/dITucM+wnkUasin0lLBs0khlyRJUs139vDo7xwhjfmI7/0g620njh9X6vJ7bhsPwDmjrwKg+9Hfq3BdyRTvmmzvASeWu85Vo88H4KgBlfvbSdgutDP+tjtTzxV/XJrBxx4BwIhhGa5r8jAGapvjjzoUiPsqjN0wzaSqx1WSpOr2s5/9DIiTuG+99VYAunTpUrCaVL1C6no49hdffDEQj43x48fndf8mckuSJEmSJEmSJEmSJEmSJEmSJEmSJElSjpnILUmSJEmSlLBlyxYA3n//fQBeffVVAKZPn55aJywL64RtOnToAMDuu+8OQJ06dSq073r16qUehxTvU0+N0pZ+97vfAdCmTZsKtSnVNMnU6zEd+wNw4e59AGhSP0qff3z5HAAumjMFgHuWROm+E/Y+rsL7TKZcTzno+0DJROTFX68D4JBXb0/bd2mJ3Mk2X+t7IVAyKWzWuiUADH3zrwA8sfwDoGR6dmlJ3FVtsyKWHHFFpbfNtZC6/tIh0Z3c7/j0dSBzWvnPOx+denxIk92AiqVd51tIIwt1Xjv/eQBGd+wHlJ0uF8ZkSLcO50tpCWf59sjSKB1uYMu9ADi6eedS1+u+S8mfU8u+2QCUrHv6mkVp87vv2LTM+WdXzU+rIdl+eXXkW3hfkyRJ2t4VT7oOjy/uEP3+e1FRgm3jekXXfiv+C8CoD54E4J6idN8JXY6t8H6TKddTepwFlExEDr9n93l9ctq+k4ncpaVmzzx4JFDy9/g31n8GwNC374v2vWIuUDJROdlmpvYq0ma2Fh86tlLb5UtI3P5Pr/MAuGPJLCBzWvnPOx0JxNd92SZdV5fUtV9RndcueBGA0UWfd2S69gvjMSRbQ3y+FOTab3n0eefAFlESzTHN9yx1ve67tE6bX/bNRqD0mqev/TRtvkPDJmXOP7v647QakvvIVEN1Kf4eJ0mSpNqrT8/uAMx86gEAHvvnVKD0lOeQPnzwgQcAcXpx0mlDjwdgw4bod9dRV11XajtnFSVEf/nV13E9g84A4D8zomujmpzI/YuxowBo2qQxAFdefzOQ3i+jzxsB5C6xOexzv67xdULoq5CoHYS07KHHHQVAq5YtSm0zH2OgtmnaeBcA7ro5GqtPv/AyAA8+/hQAT06dBsDIEcMBOGXwQMAkbklS7fHwww8D8Nvf/haA66+/HjCJe3sWjv24cdHvjFdffTUAvXr1AuLv6+aaidySJEmSJEmSJEmSJEmSJEmSJEmSJEmSlGMmckuSJEmSpO3OsmXLgDhVO0xnzJgBwMyZMwFYv349AI0aNQLiO+4BDBo0CIBrr70WgD59ipJ1dtstrY2+fftmVVP9+tHHNB07xolKkydHqVDHHHNMlq9Mqh1Com5w3m69gTiJOwgp2KWlYVdUMm16xaYvAHh/Y/R+sPirKP1qdlHaWTZCInBI4J1SlIp9QFEaVI9d2gJxMnR5idfJ9nLRZm3Xeafo7vghhf2yTocB8MmXawD478blAPy7WBJWSLsOydVXdDq8eorNwhHN05MTXl+3GIDdWmVO5A7rBENa7ZP7wspxw4L/AHDLwukATO19LlDynA32LUrVKy6cYyFtPZyDyaTxZJthPjwfaghjISS9hfaL22OnZmW/MEmSJOVNSNMt7rx2PYE4iTsIKdjJNOzKSCZOx9d+0bVDSD6eveHzrNoLacDF03efKErFjq/T2gBxOnR5qdfJNpPtVabN2q7zTs2BOIX9so4DAFj41VoA5nwRHb9nij5PCEnXIbUa4IqOh1ZPsVk4vFni2m/9EiBzInd4vrghLbvmvrBy3LAwSl27dVH0GekzB50DlDxng/12Tr/2C+dXSFoP5x/EaePhmCXbDPPh+VBDGAupa7+v06/9OjX0uk+SJElV12O/rmnTkPxcFeedOSxtmo2vFrxRoeUVbSfX2wCMGXl22rQyKrrvkHpe/PFt1/+s0vuH/IyB2iYkc4c+Ld7PkiTVRuvWRZ8l/vjHPwbgoosuAqBfv34Zt9H2JYyFMDbCWDn++Pj3oCZNMn+nq6JM5JYkSZIkSZIkSZIkSZIkSZIkSZIkSZKkHDORW5IkSZIkbRO++eab1OPZs2cDJZO2w/z8+VF6T506dQDYZ58o3TSkZ59yyilAfMe97t27A3FqdjYOPPDAtG02b96c9nyDBg3SarjmmmsAuOKKOF13hx12yGpfO+6YnmDz7datANQraluqaUICbxBSlapDMlm4Kn5alAgcErRDEnQQErZHFiWOH5ZI5CqvvVy0WRHtp92Qk3bymRIexkqYhmTyEe0OTK3z0ppPADjt7QeBOPGs+DqFEtKow3G8aM4UoOzU+bBO2Ca0UR0yJXGXV0PbHXcpseyjL1cDMLBoPqSqBz2LkgYzGdCsY1otYfswFkL7xe1ZlOqXT+s2f502H46TVJN9vfXb1OOdCliHpG1T8fcYbd9C+m5x1Xrtl0gWrqyQ8lw8kTukQQchYXtk+14AHNq0Y4XaTLZXmTaztdvLN+WkHchvSnjy2i8kk49o0wOAl9cuBOC0dx9ObbPbjo3T1imkkEgdjuOoD54EMqfOh+fD+sXbqA6ZkrjLq6HNDo3S5ucnrstConpxPXdpW2abhzbtkFZLaCOMheQ+qiORe/23X5dYVvxYSQD16pqnI0mSJEmSVJNcd911ALRs2RKAYcOGFbIc1WBhbDz77LNAPHYAbrghN9+nAxO5JUmSJEmSJEmSJEmSJEmSJEmSJEmSJCnnTOSWJEmSJEm1wiefRAmr06dHSZwhXTtM33jjjdS6X38dpaS0aNECgD59+gBwzjlRmkxI3g7T5s1zn9gZUrL33HNPAObNm5f2/NFHHw3ApEmTAOjcuXOl99W0adO0+ZAS06x+w0q3KW1r7v0sSoMLab7ntDsIgCGt9gGgRYMok7T1DlGScI/pvy+3zZBKHBKo39+4DIBpq6P3q5CmHRK2Q1JvSN5Ophon28tFm7VNMhW8MuneyZTyy+c9DZRM5A59F/oyJCs3qb9j1vuqbBrz99v2SNv3rHVLgDhhPMyXtk0+rdj0BQB3LZ4FwHsblwPw0iHnA9B5pxZZtRNS0IsLY/fC3Q8B4LV1i9Oe77ZL2WN330QSXNg+9FkyvT5THbn26dfpCXPHmcqmWmDd1m9Sjzs2yf95Imn70LhxlIS7aOumAlei7d29S98G4jTfs9tG1wFDd+0KQPP64dovShI+cOakMtsLicTF06ffL/o9+T9riq7TihK1Q8J2SOoNydvJVONkm8n2KtNmbZNMBq9oundpCeVXfPgMUDKRO/Rd6MvwuVnjetld+1UljXlEm+5p+35j/WdAnDAe5pPr51O47gO467PZQLEx2Os8ADrvlN1npclrrjBuf7TbwUDJ6z4of+zus/OuafOhjdBnyQT76rjuW/TVuhLLBrao/Ge52raEz4aaNvbaSpIkSZIkqSb48MPouzi33norALfccgsAdeuah6zShbExevRoAMaMGZN67oILLgBgr72y+05YmfupcguSJEmSJEmSJEmSJEmSJEmSJEmSJEmSpDQmckuSJEmSpILasGEDAK+//joAM2bMSJuGxO3PP/8cgPr1o48zunePEmr69+8PwI9+9KNUm/369QOga9cobalOnTr5ewHlGDZsGAC/+c1vAHj44YcBGD58eM72kbzb38dfrgagZ1FSjVTThDTsez57E4jToHZtsHPe9hlSmYMJex9X6nrJhOWKCGnYYTq0KO07nJOnvf0gEKcwZ5M2nY82M6nKtrny885HA3G68ktFiXTJlO2yZHsM+zftAMR9N++LlUCc8JyNtzd8Xmqb5embWO/ZonS2sO8wX9Y2uRSS33+z4CUA9i9KSLup6wlA5c/NMR37px7fsnA6AIu/jpLMwjEOKeblpaiFGsL6YftwTmTab749sfyDtPmeTfzZq5pvwVdrUo+/29kkQUm50aVLlE775NdryllT24uQhA3wl8/fAqrn2i+kMgcTuhxb6nqlpSxnKyQKh+mQorTv8DP2tHejz35CCnN5adPJ9nLRZiaV3S7Xft7pSCBOV3557UKg9KTt0lTk+PVvsjsQ9928L1YBccJzed7esDRjm+Xp1zR9vamr56ftO8xnWj+XQur2DQtfTi0LY+7GvaLPSCp7bl7cIfos9tZF0ee5qeu+YunZIcU862u/ovVDG+GcSO6zOjyxcm6JZT138dpPkfA+3aVzlwJXIkmSJEmSJICbb74ZgP333x+Abt26FbIc1SJhrBQfMyHR/fe//32V2zeRW5IkSZIkSZIkSZIkSZIkSZIkSZIkSZJyzERuSZIkSZKUc1u2bAHgv//9b2pZMmE7zL/33nsAfPvttwDstttuAPTt2xeASy+9FIhTtnv16gVAo0aN8vcCcmjChAlp03zo1KkTAK1atATgrfVRQqyJ3Kqp+jeLkoVDIvddi2cBcOHufQBoUn9HAB5fPgeAi+ZMAeIk70xp2hUx/8sogavzTi2AOMX59k9nZt3GlfP+DcSvY8pB3wfiVOXyUqbKay8XbdY2x7UsStwqSlsOieMP9TgdgB67tAXiMRKE4wnwyNL30p6btN/QUvd1RPP0lO+hb/4VKNnnpZm1bgkAk4vGbqY2Mwn1JxPIhxSlS4f06uLrJF9zVYWENIBjZ90NxEnWV3Q6PCf72KfRriWWTUkkWA9rvV+F2gzrhyT1ZHuZ9psLIUES4OkV84D4WIX3p26NWudl31IuhDH86YbVqWV9+vQpVDmStjGHHHIIEL/HFP+5mc/0ZdVc/YslC4dE7rs+mw3ARbsdDEDjekXXfiuiz49GffAkEKd5Z0rTroj5X0ZjsvNOzYE4yXnS4tez2v7Kj6YC8WsAmNLjLCBOVa7wtV+izWR7lWmztkmmLYfE8YcOOBWAHru0AeIxEoTj+cjy90u0OXGfwaXu6/BmiWu/t+8DSu/34t5Y/xkAk5e8UW6bmYT6kwnkQ1pG6dIhwTo8n3y9uRCu/Qa+eQ+QnmR9RcdDc7KPfXZumTb/xIqSCdbfa7VvhdoM64ck9WSbyX3mUvgZ9vSq6LozHCeI359Cmrn0ztcrADikf+nvQZIkSduKrxaUvDaSJEmqSTZt2gTAX/8aff9n1KhRhSxHtdgJJ5yQejxx4kQgTnpv0KBBpds1kVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmScsx/5JYkSZIkSZIkSZIkSZIkSZIkSZIkSZKkHKtf6AIkSZIkSVLts2LFCgBeffXVtOmMGTMAmDlzJgBr165NbbPTTjsB0Lt3bwAGDhwIwDXXXANAVAtoswAAIABJREFUv379ANh9993zWvu2bPCQIQA8+9QrAJzbvmchy5EyOqnVfgA8umwOALcsnJ42zeSc9gdVep+T9hsKwEVzpgBw2Gt3Vmj7+V+uAqDzTi1Sy05tcwAA93z2JgBD3/xrVm39du/jS12ebC8XbdY2oX+Tx+u0tx+scFtjOvYH4vGW1K1R67T1wvjLts+LC/WGNrN1RPM90uaPnXV3uevkygurPi6xLNtzMWnJEVeUunz3HZuWWHbt/OfT5vfeuWWF9tV9lzZltpdpv9loP+2GCm8zsOVeAFzW6bBK7VOqTs+umg9As10ap5YNGDCgUOVI2sYceuihADQteo95bnX8u8ZprfcvSE0qrJN23Tf1+LHl/wXg1kUz0qaZnNP2wErvd+I+gwEY9cGTABz+xl0V2n7+l6sB6LxTcyAev3/5/K3UOkPfvi+rtm7Ya2Cpy5NtZtteWW3WNqF/k8frtHcfrlA7F3fol3pcfMwV161Rq7R1w/irSL8Hod7QZrYOb5Z+XTfwzXvKfD6XXlizIG2++PlX3rmYtPjQsaUu75C4Brt2wYsl1tl7pwpe+zVKXPsl2kzusyJ2e/mmCq0/sEWX1OPLOvr7syJrN38FwOtrPgVg3ODBhSxHkiRJkiRpu/fyyy8DsG7dOgD69u1byHJUixUfOxMmTADglVei7+QeeeSRlW7XRG5JkiRJkiRJkiRJkiRJkiRJkiRJkiRJyjETuSVJkiRJUsqmTZsAeOutKBEoJGwnk7fnzZuXtl3Xrl0B6NOnDwDXX389EKdsA/To0QOABg0a5KV2wQ/+37kADPxrlCa79JsNALTZYZdClSSV6baiJKspRelsl897Ou35kJQ8vE2UVlY8DbuiQirzhs3fZLWvr7ZsBuKE5OlrFpWooXeT9gBM7X0uAE8s/wAomWYc2u7ZuB0QJ/gmJdvLRZu1VTheBzfZDYjTo/+96iMAnln5Ydr6oT8ABjTrCMBhWSaaXdHpcACGtNoHgGmrPwFKT3oOQgJ6/2YdgMqPzZDgHY5feF3Fj2dFU76zlTwH8qHtjuX//Kno68umr7PZb2Wc0+6g1ONw7I9u3hmAJvV3zMs+pVx6aOX7AIw45+zUMn83l5Qr4f1kxNnfB+Chh59KPWcit27rOgiAf6yIrm+u+PCZtOdDUvLwVt2AOK25MkIq84Zvv8lqX+HaLyQkT18Xrv2iGnoVXXM9c9A5qTaeWDkXKJlmHNruuUvbqM1iKb7FJdvM1F5F2qytwvE6uHF0PRzSo59ZNb9o+lHa+qE/Dm3aoWjaMet9XdHxUACGtIw+x/zPmqJrv1LSoyFOP+/fpENqWWXHZkjwDscvvK4wX9GE74pIngP50GaHRuWuU9HXWF5fZ7PPyjq77YEA9G+6OwDHNN8z9Vzjel77KfJY0eeZu7bYFYDjjjuukOVIkrYBDTv1KrHsqwVvFKCS7VNp/Z+UPB5r10d/i3/6hSj58cHHo89Cnpw6DYDBxx4RTb8TTYcedxQArVpW/u+dlRVq/dsT/04te/LZqM5kvaefFF3DH39UdA3VtHHN+a5BqPWU88ekltWE8yTZvzW9b2tTveXVCjWr3kySY7cmjNuyVLTeyryHSlI+vPbaawC0bRt9nt68eeX/1qDtW/GxE8bTzJkzARO5JUmSJEmSJEmSJEmSJEmSJEmSJEmSJKlGqbN169athS6iNlmxYgUAzz8fp7GElLLPPvsMgPXr11d/YZIkSZJUjXbcMUqdaNEiulPwAQccAMDhh0eJlvvuu29hClOZFi5cCMSp2iFtu/jj2bNnA/Dll18C0LRpUyBO1u7bty8QJ2+H5S1btsxr7cpO+Jin/8HR8TloRT0AftGp8ncBlCRJUu02a90SAIa/9yAA777/fuq5vfbaqyA1Sdp2ffjhhwAc0K1batkj+50KxCnEkiRJtdU3W74F4Jj37gXgx7+4EoBLL720YDVJuTRixAgAtmxcDcDdt15fyHKk7YqJ3IVVkTTZkBB83iXjgPRk4LKE1ODbf/NzoHqTuUdf/WsAJt/7SNbbhHr/ducteampIt6eMxeAPoPOKPFcTThPKtq/he7b2lTvtjp2a8K4LU1l6zWRW9uzcy++GoC6jaL03nvvvbeQ5dRIK1euBODbb6PPVFq3bp23fV144YUAvP766wDcdNNNeduXth9jx44F4JBDDgFg0qRJlW7LRG5JkiRJkiRJkiRJkiRJkiRJkiRJkiRJyrH6hS6gptq8eTMADz4YpRPccccdALz88ssA1K0b/w98t6K7ibdt2xaAJk2aVFudkiRJklQIa9euBeDjjz8G4J577gFgzZo1AOy5554AnHvuuUB8pzvI7x31tlcbN24EYNasWUCcrh2mIYF7yZIoia9evSilOSSpQ5ysPXLkSCBO3g7p6nXq1MnfC1DOhON068TfA3DEYYcBMKJ1dKz32tnkdEmSpO3FFrYCcM2iFwD4yU8uBkzhlpRf4T0mvOcAXPN/DwAwZb/TAKiLnzFIkqTa6Y7Por/DNGgZfT9w9OjRhSxHkiTlSVnJsU+/EP0vQUjinjg+SuY+ZchxADRtvAsQJ3ffckf0fZrxt90JwL2PPgnAmJFn57rsEkK6bkgzvmr0+annzjtzGAAd2kf//7BoyecA3PCHu9K2mffxJwDsveceea83aebsdwA44ns/qPZ9ZyNT/9bEvg211rZ6y6u1ptSbVNPHblJV6y3tPTOblG5J24fBgwcD8XdYv//97wNwySWXANCrV+7eL9avXw9Ao0aNctZmTfLRRx8BMGfOHACGDBlSsFqeeOIJAPbbbz8AunTpUrBa8i2Mp3Xr1lW5LRO5JUmSJEmSJEmSJEmSJEmSJEmSJEmSJCnHTOROeOGFF4D4bpkffPABAMOGRXfvmTJlCgBHH310apuGDRtWY4WSJEmSVHPNnj0bgIcffhiAP/zhDwDcfPPNqXXGjYvuRhyuu3bYYYfqLLHW2Lp1a+pxuDYNdyUM0+nTpwPw7rvvArB582YA2raN7jwbUrZ/8pOfAHHK9iGHHAJsu3ceVHyszzz9DAAu/9dUAB7qNhyABnXqFaYwSZIkVZs/LY6S4j7jKwDG/fyaQpYjaTtT/D3nnrvvBuBPS6JklpHtexeiJEmSpEr58MtVqcf/u+Q1AB58NEr5a9CgQUFqkiRJhfPg40+lzYeE4KSQzD3mgnOAOJH7yuuj789URyL362++mzZ/1rDBqcfFE42Lz4/8fvSdgpBqPPvd/wLVm2p8y+S/AHFf3XPbeADOGX1VtdWQjUz9WxP7Nlkr1K56M9VafJljt+JqW71STfbFF18AsHLlSgBatmxZyHJqlFWrVqXNP/jggwD89a9/BWDAgAEAXHrppQCcfPLJANSrV/nvN25r30leunQpAH/6058AuPrqqwtZDhD/X21IXA/HtU2bNgWrKV9yOZ5M5JYkSZIkSZIkSZIkSZIkSZIkSZIkSZKkHNvuE7k3btwIwMiRIwG4//77ATjxxBMBeOyxxwDYa6+9ClCdJEmSJNUuPXv2TJuG9O1f//rXqXWuuSZKZAp3hwt3YuvevXu11VkThDsNhnTtGTNmADBz5sy05QCrV68GoGHDhgD06tULiO9qd+WVVwLQv39/ADp27JjX2lV7TLjxtwD0+Of+AIyb/xwAv+kysGA1SZIkKb+eXTUfgGs/fgGA+x94AIAmTZoUqiRJ26Hi7zm3TZoIwJlnnAFAl51aAHBM8z2rvzBJkqQsrdz0JQDnzpuSWnbCiYOAOG1IklQ7NOwU/X195IgorfW263+W1Xajr46/5xASXpe+Mw2I05bfnjMXgOdeiv6+H1JNg8HHHgHA6SdFP0NOG3p8xV9AkfA6gq8WvJHT9QFeeOU1AP725DNA/LrD6xh93ggAjhpwSBYVZydZZ1Vk8xqr6m933lKh9cNYKYRFSz5Pm2+9a/npoO1a75o2P2fuRzmtKRvhPAp9HcZfTUsJrmj/FrJvk7VC7arXsZsfta1eqSabOnUqALvuGr0X1a0b5e6GZO7ktEWLFmnzpa3TqlWrUtfNNK2pKdTh/zaDTZs2pc2H78meeuqpALRv3x6ASy65BIAf/vCHADRr1iyvddZk9957LwDDh0fXM40aNSpkOWk1/O53vwPiGkOyukpnIrckSZIkSZIkSZIkSZIkSZIkSZIkSZIk5VidrVu3bi10EYWwePFiAE466aS0+bvuuguAQYMGFaYwSZIkSdrGLViwAIDzzz8fiBOoQzJ3bb8e27x5MwBvv/02ECdtJ5O3582bB0C4LO/SpQsA/fr1A6Bv376pNsPjkHTeoEGD/L0AbZPC+DvqiOjuuZfsFo2z0R36FawmSZIk5db7G5cBMOzd6Npq1JifADB+woSC1SRJxV115ZUATLzlfwH4235RukK3Rq0KVpMkSVLSV1uiv/OM+ODvAGxsG6dozngt+pvWzjvvXP2FSdVgxIgoaXfLxtUA3H3r9YUsR8qZWyb/BYiTRxfNihITW7VsUer6y1euAqBD72NTyyZcHSUCjhl5NgBPTo2SuU85f0yFarnntvFAyWTu0lKpk+nS+Uzk/uVNEwEYf9udZbYZXDU6+r7HL8aOymr9stSERO7KpJdna97HnwDQ/ejvAZnHQD5U5XXls08qK5vzpDpVto8K0bdV6buaUO+2NnZrQk1lyUW9te01S5V17sVXA7BuUx0gTpFesWIFAKtWRb9Xrly5ssxp8cdhm+XLlwOwdu3arGpp3LgxECd4h3TwMC0r/TtT6neyjbBe2Fc2wmcoX375ZdbbANSvXx+Ivyt73nnnAfCTn/wktU7Xrl3TtgnXtMuWRX83HzduXIX2WdO88Ub03hlSrp988kmgZiRyByFxffDgwUCc0A3Qq1fufs8vpOuuuw6A1q1bA3H6eGWYyC1JkiRJkiRJkiRJkiRJkiRJkiRJkiRJOVa/0AVUtw8//BCAI488EojvEhGSqTp27FiYwiRJkiRpO9GpUycA/vWvfwEwevRoAIYOHQrA7bffDsSJ3TXJ4sWLU4/DdeT06dPT5mfNmgXAF198AUCTJk0A6NOnDwCnnhqlTSWTt1u1Mn1K+RPG2Z133QXA2WdHd4pfu/nr1Do/2zNK665LnWquTpIkSVXx4uoFAPxo7hQABg46AYBfjx9fqJIkqVThfWneB3MBOOWfDwNw+94nAnBks04FqUuSJAlg2TdRgtB586Jrq2UNtwAw4+l/pdYxiVuSaqdjDuubNv/8K68BmRORw/PFDT72iLT5ZBL3tMf+DECfnt3Tli9a8jkAew+Irn3PGX1Vmfuubi8UvdaQxB2StsdccA4ATRvvAsDa9RsAuOWOe9LW/96JUWp5j/3S0xArYltPab3v0Sg5MYyh4486tJDlSJKkPGjWrBkAxx57bM7b3rx5M5A5zTtT6ndI9E6uB7Bo0aIy2wz7zGSHHXYASiZ4F0/9bt68OVDxJO4g1BCmd9xxBwATJ05MrXP88dHv1GPHjq3UPmq6Rx55BIDLLrsMqFlJ3EGoKdQYaoZtJ5E7l0zkliRJkiRJkiRJkiRJkiRJkiRJkiRJkqQc224SudeuXQvA4MGDAdhnn30AePzxxwFo3LhxYQqTJEmSpO1U/frRJemkSZMA6Ny5MwD/8z//kzZ/zDHH5L2WcNe/119/HYCZM2cCJdO2P/3009Q29erVA6Bbt25AnHh87rnnAnECd3i+bl3vpabCGzFiBBDfFfMHRcncAB9/vQaAW/ceBMAu9Xao5uokSZKUja1F07uXREk1v5j/PBBfi0y8PbrGqlOnTnWXJkllCu9L9z/0IACjLrwIgHPuvhuAX3Y6CoAftDsoWr96y5MkSdup9zYuA+Ik7ia7twXgP0/9E4Ddd9+9MIVJknImpEWHROQHH38KyJyKHZ4fOWJ4atnee+6Rtk4yRXr5yijh8O05cwFYtDhK4n79rXerVHu+vTg9PX08mcQdhPnwfEjkfu6l6LsUVUnk3lb98qYoLTL01cynHgBK9q0kSVJZwvds27RpkzbNp/A/mCGhe8WKFWnz5aWDQ/p3bXNh06ZNJZZNnToVgH/9619py8N3d2ur999/H4BXXnkFgO9///uFLCcr4fveN954Y2pZeB3hO9QykVuSJEmSJEmSJEmSJEmSJEmSJEmSJEmScm6bT+TevHkzAMOGDQNgxx13BOCxxx4DTOKWslHR1I6hQ4emTU866SQAWrdundvCpBrsrbfeAuI0zylTpqRNx40bB8DgwYMB6NevX3WXqGKS73Nbt24tdb2FCxcC0LFjx7zXpJLC3b0eeughAC644IK058N5BXB2Ubpo1665vdtrsgYoeX6Hn39nnXUWAIMGRammTZs2zWkt0rbo8ssvB2Dp0qVAfB0Xfp5W5ZyeOze663VI1p4xY0bafPjZHa4hw10LQ8r2RRdFCVH9+/dPtXnwwQcDXleqdjr11FMB2GOP+M7xJw0eAsDhs+8C4MrdDwXgtLbdAZPQJEmSCu3dDdG10s8XvADAzNWLAJjwmwlAfE0lSTVdgwYNAJj8pyiRquu++wBw5U+vBGDK6nkA/HKPIwE4oJF/Y5QkSbmzbvPXAPx2UZRqdM/n0d+Ijjn6aAAe+tsjgH/flaRt0ejzRgBwwlk/AmDex58Acdp2mH9y6jQA/nXfH8ttM5m6XNsk627T/YgKbX/l9TcDMGbk2ZWuoWGnXpXeNimZlF4ImZK4TS2XJEm1RfhMJExD2nJFvPvuuwA8/vjjuSsMqFevXupxpv/52LJlS073Wd3mz5+fNt+yZcsCVZK90moMr8NE7piJ3JIkSZIkSZIkSZIkSZIkSZIkSZIkSZKUY9t8IvfEidFdrd555x0gTlvzjplS/iSTScP0zjvjO/eZzq1t1U033QTAZZddVuZ61113Xdo0JPj+6le/AuDAAw/MV4mqhORxzXT3JuVXSNkOP1eSwvlU/PGbb74J5O6cuvLKKBHm9ttvz7hOpoTuf/zjHzmpQdoe/OY3vwFg3rwofen8888H4MUXXwSgTp0oF3jt2rVAyXTt4o/Dc6tWrQJgxx13BOCggw4C4LDDDgNg7NixAPTr1w+APffcM7cvSqqh+vTpk3r8zpz3AfjFz38BwOV3RHeY/8vy6DOVC9v1BuD4lnsD0KCO9weUJEnKp3eKErjvWhKlyDy8NLpreZ+DDwFg5qS/AdC7d+8CVCdJuXP55ZcDcMwxxwDw44tGATDo9b8CMLz1/gD8sF1PwIRuSZJUMSs2fQHAA0XXVH9cGl1j1du5YTQ/+Q4Azj33XADq1vWzb0naVvXsvl/a/H9mzALiRO7Z7/63zPWLu+v+R4E4dXnkiOEAnDJ4IAAtmkffU2/XelcAOvQ+tkq1q+ZavjL6PsrEu6Pk7bfnzAXgnecfA+LxVQhXjY6+bxPG6dr1G1LPNW28S6nbFF+neBsqKVP/1sS+Lb6v2lRvtrUWXyfZhiSpMFasWFGp7cL3g+vXj/7lddOmTQA0adIEgGOPjX+vPv7449OWXXPNNQAsW7asUvuuKV555ZW0+TZt2hSokuyVVmN4HUOGDKnucmosP3WUJEmSJEmSJEmSJEmSJEmSJEmSJEmSpBzbJhO5ly9fnnr8i19EKVKTJ08GTFWTcqG8JNqQjHjjjTcCcSrqc889l1rnjDPOyFN1UmEkE5svvPDCtGkyDTicJ7NmRXf1/M53vgPECb6ffPIJAB07dsxn2SpS3vtaeQnryq8HHojuVhrOjzvuiO6IPnLkyLT1iv+cCedUSM6eNGlSlWp466230tobN25c6rlQRzhfFy5cCMD48ePTtpk7N7rbateuXatUi7Q9qFevHgB33XUXAPvssw8A999/PwBnnXUWAN/97ncBmDZtGpB+vReStcMd9sJ8z55RclNI5pYU23XX6G7wf5j4BwBGXhD9jPtl0WcrFz0R/SzepUGUUnJo0w4AHLBzlITWZodG0fP1PL8kSZLK89WWzQCs2fwlAB9sjO5IPv2LJal1Pl4XLTtgv24A/OWmKJn2zDPPBOK7kUvStqJ3794AvPLqDCD+LGj8ddcDcPybfwFgz8bR9euAndsDsPfOLVJtNK+/EwAN626TX4WQJEkJ37Il9Xjt5q8BWPDVGgDe/DJKX3p9zacANGscJTddMGY0AD/96U+j5c2aVU+xkqSCC0muE8dH3/sZdVX03dahxx0FwDmjr0p7vqzk17BtcNv1Pyt1vWRCbHUICdHZCEnik+99BICl70TfvyjrtefaVwveqLZ95VJI3v7lTRMB6LFf9J2w23/zcwBatWxR+obVaL+uXdLml61YmXqc6Rh/8umStPkO7dvmvrBtRKb+rYl9m6wVale95dUKjl1JqmlWrSr7d9IddtgBgG+++QaABg0aANC/f38ATjjhBAAGDhwIQK9evQCoW3fbzzROJnLXVtvK68ilbX/0SpIkSZIkSZIkSZIkSZIkSZIkSZIkSVI18x+5JUmSJEmSJEmSJEmSJEmSJEmSJEmSJCnH6he6gHy45pprUo979uwJwPDhwwtVjrTdadq0KQCXXXYZANdddx0A9913X2qdM844o/oLk/Jg7ty5QDzehw4dCsCkSZPK3C6cJ8cccwwA06dPB6B///4APPzwwwCMHTs2xxVLsbfeeguAqVOnppbVxDFX/OcHwGmnnVbqeuF8Ku72228Hyj8nyzNz5sy0+bPPPjv1uGPHjmnPhfkLL7wwrYY33ngDgK5du1apFml70rJlSwB+9atfAXDVVVcBcPLJJwPx+dWiRQsA2rRpU90lStu0gw46CIDHHn8cgM8++wyAJ554AoCpzzwDwN9nRT/jPl+8FIB1GzdUa52SJEm1UcMddgSgRbPmAHQ7YH8A/t9R8d+zTjzxRAB69+5dzdVJUmHVqVMHgLPOOittOmvWLAD++c9/AjDthRcBeP7d91LbrlqzGoCvvvm6eoqVJEkFVbdunGPTrHETALp07gzAIf2HADBu0CAAjj32WAAaNmxYnSVKkmqgw/ulf97WofexafMDjxxQ4TbnffwJAHvvuQcAa9dHfzO95Y57KlNiqQYfewQAT06dBsDM2e8A0Kdn97R9Trz7gazbPGXwQAAm3/sIENc76tzoO76tWrZIW/+FV14D4ISzfgTAhKsvAWDMyLPZXixa8jkAfQZFfXTV6PMB+MXYUQWrKZN999ozbf6+R59MPT7vzGEAdGjfFohf12P/nJq2zcEHHZDPEmu1TP1bE/s2WSvUrnoz1Qo1o15JUkkbN25Mmw9/+9l3330BGDx4MBB/XnP44YcDsPPOO1dXiVK1M5FbkiRJkiRJkiRJkiRJkiRJkiRJkiRJknJsm0rk/uST6G5md955Z2rZSy+9VKhypO1eSBwOpkyZUu42yXTYkHIchLTjcOf9bJK9n3vuOSBOOA7JjcG4ceMAGD48Sjo58MADK9ReLtrMptbw2seMGQOUnjxbXLhjTbB0aZSO95e//AUomSANFetXgAceiO7cGNJywzEOrz8k1u6zzz4ltt26dWuZbWfqi4r2A5Tsi/Dz4sc//jEQH5+Q9lkRL774Ytr8z372swq3AdCvXz8Ann32WQD22muvcrfJ1fmS7B+Ij084xmeeeWba8/fffz8Ag4ruXJ083zPVmot6g7Vr1wLw1FNPASXHIcSJyJdcEt35M1MScrIPwusvrW8yLQ/177bbbkD5CdCh/mbNmqXVWtXk6LLMmDEDgD//+c9AyfcuKJnInakPKqq8c74s//jHP7Jar7SfM2GsVtXChQvT5rNJ/W3Xrl3a/HvvvZdhTUnlueCCCwCYMGECEL9/XXrppQWrSdoehZ9tI0eOTJtKkiRJklQdevfunTblmmsKWI0kSZIkqbYKqdkjR0Tf7wxp1GG+eNJrJvfcNh6Ac0ZfBUD3o79XoRqSCd7ZOP2k6Lt6IZH7iO/9oNT1Qkp2No4acAgQp0qPv+3OtGkmIR18xLDBWe9rW/HMi6+kzWfbZ0lfLXijxLKGnXqVu05F9Ngv+r5kOF7Fayyv3nA+hDaScl1rvuWj3kz9W9W+hfyNBch9vTWpb8GxK0k1xcknnwzAH//4x7T51q1bF6ym2uK73/0ukP3/UNRU4XUoZiK3JEmSJEmSJEmSJEmSJEmSJEmSJEmSJOVYna1ViUesYa4puuP2448/nlr29ttvF6ocaZuRKam2PMm02eLJz8k7g4QU1YrecSMkrZaW3FvZNkMicjLpubLtldVmEN6/rrvuuqzbDKnXmVKkk8ct9H82yehl9StUrt6kTOOoom2X1w9Qsi/CNmEf5b3esoTxEPp1zZo1QPkJ1VWR6/OltMTlcI6Wt48wrjLd7acq5015xyXZ99l48803gTiFPahoIndpwnn+ne98p8x9BSEdu3///kDch8XfKysrvP9OmxbdfXXy5MlA3Fch/fvEE08EoG/fvqltk3e5qgmJ3JncdNNNQMl0d6jaeV2ayv4srOq2ktJde+21ANx3330A/Pe//y1kOZIkSZIkSZIkSZK0zRgxYgQAWzauBuDuW68vZDlS3r3wymsAnHDWjwCY9tifAejTs3vWbdx1/6MAjLoq/fuOIeH6rKLE6i+/+jpqe1D0XaaJ46PvMJ535jCgZOIqZE5dfWjK0wA8+PhTQJzQXV6b2aS4hrb/M2MWEKeVB2EfQ487CoBWLVuU22ZtUJG+Ku1YVUZ1JHIHy1euAmDKv19ILXvy2WjchPETko8HfyeanjLkOACaNt6l1DYLkRJckfOkvG1zWW+yf6vat7Wt3kLWWtPqzaQq+6xt9eayDak2OPfiqwGo26g5APfee28hy9nuhWvaZcuWAfH/DdU2TzzxBAA33ngjAA8++CAAbdq0KVhN5Vm6dCkAp59+empZ+B+PIUOGFKSmXAn/dxb+z6Yq57mJ3JIkSZIkSZIkSZIkSZIkSZIkSZIkSZKUY9svcMyYAAAgAElEQVRUInePHj0AOPnkk1PLQmKbpMqraJJouHvJbbfdBpRMPYbyE4GnT58OQL9+/dKWL1y4EIA99tij3JqSbX7yyScAdOzYMW15MhU3JNVOmjQpq/aq0uZzzz0HxAm+4Y4v4c4jxVOdQ8JuuKtK6NdsE4YztR1qKF5HppTlTPWOHDkSiPshHKfx48cDcPvtt5OUPGbZ9kVF+wFK9kUuk3oLkbSb6/OltMTlMAZ+//vfAyWPbUh4Dv2fKXW+tLarWm8y5bus8+aBBx4A4MwzzwSyP7/L66OyjnOoK4zFTGnxyTTpDz74AICuXbtmbDuT0HevvPIKEL/ecBzPOussAAYMGACUfM+qrcL7xuzZswF48cUXU8+FcZKr891EbqlmeOedd4D42m/OnDkA7LvvvgWrSZIkSZIkSZIkSZK2BSZyS9pe1bTk2FBPoevIRm2qFaw3n2pTrWC9+VbT3lelfDGRu2bZVhK5P/roIwB++MMfAjBx4kQAunXrVuZ2Rx11VNr8Cy+8UOV1s13v/fffB2DUqFGpZX/6058A6NKlS8Y6agMTuSVJkiRJkiRJkiRJkiRJkiRJkiRJkiSpBqtf6AJyYfXq6O5/IZ0tJFxKyo/SEnbLEhJhBw0alHGdZDppuAPKW2+9BcRpszNnzqzwfkMq68MPPwxAz549AejduzcQpwKXl5Caqb2qtPn888+nzZeWKByEZWGdcFePqVOnAqUnURc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      "text/plain": [
       "<Figure size 5040x5040 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = plt.imread(r'C:\\Users\\Nicolas\\Anaconda3\\pkgs\\graphviz-2.38-hfd603c8_2\\Library\\bin\\graphviz/cars_tree.jpg')\n",
    "plt.figure(figsize = (70,70))\n",
    "plt.grid(None)\n",
    "plt.imshow(tree, interpolation='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We will make a randomized search for the best parameters (for the real model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what parameters are tunable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_impurity_split', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'presort', 'random_state', 'splitter'])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = tree_clf.get_params().keys()\n",
    "params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select a bunch of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_to_be_randomized = {'max_depth':np.round(np.linspace(80, 90, 1)), \n",
    "                         'max_features':np.arange(79, 84), \n",
    "                         'max_leaf_nodes':np.round(np.arange(145, 153, 1)), \n",
    "                         'min_samples_leaf':np.arange(1, 3),\n",
    "                         'presort':[True, False]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define the search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_search = RandomizedSearchCV(tree_clf, dict_to_be_randomized, n_iter=80, scoring='accuracy', n_jobs=-1, refit=True, \n",
    "                   return_train_score=True, cv=5, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run the search!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    3.4s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   11.6s\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:   25.6s\n",
      "[Parallel(n_jobs=-1)]: Done 400 out of 400 | elapsed:   29.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=DecisionTreeClassifier(class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features=None,\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    presort=False,\n",
       "                                                    random_state=None,\n",
       "                                                    splitter='best'),\n",
       "                   iid='warn', n_iter=80, n_jobs=-1,\n",
       "                   param_distributions={'max_depth': array([80.]),\n",
       "                                        'max_features': array([79, 80, 81, 82, 83]),\n",
       "                                        'max_leaf_nodes': array([145, 146, 147, 148, 149, 150, 151, 152]),\n",
       "                                        'min_samples_leaf': array([1, 2]),\n",
       "                                        'presort': [True, False]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=True, scoring='accuracy', verbose=2)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With a randomized search, we have reached a 97.500000% accuracy with a decision tree\n"
     ]
    }
   ],
   "source": [
    "y_pred = rand_search.best_estimator_.predict(X_test)\n",
    "tree_accuracy = accuracy_score(y_pred, y_test)\n",
    "print('With a randomized search, we have reached a %f%% accuracy with a decision tree'%(tree_accuracy*1_00))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'presort': True,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_leaf_nodes': 148,\n",
       " 'max_features': 80,\n",
       " 'max_depth': 80.0}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97109375"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the train and test scores are very close, it doesn't seem like the model is overfitting the data. Regularization methods could have been reducing the `max_depth`, increasing the `min_samples_leaf`, decreasing the `max_features`, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.9825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Accuracy\n",
       "Decision Tree    0.9825"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_df = pd.DataFrame([trees_accuracy], index=['Decision Tree'], columns=['Accuracy'])\n",
    "acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Accuracy of various models')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(data=acc_df.reset_index(), x='index', y='Accuracy', s=15)\n",
    "plt.xlabel('Model')\n",
    "g.set_xticklabels(rotation=30)\n",
    "plt.title('Accuracy of various models')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### #2 Classifier: Random Forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_clf = RandomForestClassifier(n_estimators=200, n_jobs=-1, oob_score=True, min_samples_leaf=2, \n",
    "                                    max_depth=22, max_features=80) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=22, max_features=80, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=2, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "                       n_jobs=-1, oob_score=True, random_state=None, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = forest_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98625"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_accuracy = accuracy_score(y_test, y_pred)\n",
    "forest_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=22, max_features=80, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=2, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "                       n_jobs=-1, oob_score=True, random_state=None, verbose=0,\n",
       "                       warm_start=False)>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.get_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's an even better score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Random Forests take just a sample of the data, we can use the rest of the data (the out-of-bag data, or _oob_) as a test sample. That way, we can estimate the performance of the model before we make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98609375"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's almost the same! We didn't even really need a holdout (test) set!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.98250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.98625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Accuracy\n",
       "Decision Tree   0.98250\n",
       "Random Forest   0.98625"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_df = pd.concat([acc_df, pd.DataFrame([forest_accuracy], columns=['Accuracy'], index=['Random Forest'])], sort=False, axis=0)\n",
    "acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Accuracy of various models')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(data=acc_df.reset_index(), x='index', y='Accuracy', s=15)\n",
    "plt.xlabel('Model')\n",
    "g.set_xticklabels(rotation=30)\n",
    "plt.title('Accuracy of various models')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### #3 Classifier: Extra Tree "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of splitting the attribute to get the most _information gain_ like normal decision trees, extra trees split the attribute randomly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiating the class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_clf = ExtraTreeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                    max_features='auto', max_leaf_nodes=None,\n",
       "                    min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                    min_samples_leaf=1, min_samples_split=2,\n",
       "                    min_weight_fraction_leaf=0.0, random_state=None,\n",
       "                    splitter='random')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = extra_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.941875"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preparing the grid search parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'max_depth':range(25, 35),\n",
    "    'max_features':range(60, 70)     \n",
    "         }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Launching the grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=ExtraTreeClassifier(class_weight=None, criterion='gini',\n",
       "                                           max_depth=None, max_features='auto',\n",
       "                                           max_leaf_nodes=None,\n",
       "                                           min_impurity_decrease=0.0,\n",
       "                                           min_impurity_split=None,\n",
       "                                           min_samples_leaf=1,\n",
       "                                           min_samples_split=2,\n",
       "                                           min_weight_fraction_leaf=0.0,\n",
       "                                           random_state=None,\n",
       "                                           splitter='random'),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'max_depth': range(25, 35),\n",
       "                         'max_features': range(60, 70)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = GridSearchCV(extra_clf, params, n_jobs=-1, cv=5)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreeClassifier(class_weight=None, criterion='gini', max_depth=28,\n",
       "                    max_features=65, max_leaf_nodes=None,\n",
       "                    min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                    min_samples_leaf=1, min_samples_split=2,\n",
       "                    min_weight_fraction_leaf=0.0, random_state=None,\n",
       "                    splitter='random')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the best model according to the grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With extra trees, we have reached a 97.687500% accuracy\n"
     ]
    }
   ],
   "source": [
    "y_pred = grid.best_estimator_.predict(X_test)\n",
    "extra_accuracy = accuracy_score(y_pred, y_test)\n",
    "print('With extra trees, we have reached a %f%% accuracy'%(extra_accuracy*1_00))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.982500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.986250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extra Trees</th>\n",
       "      <td>0.976875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Accuracy\n",
       "Decision Tree  0.982500\n",
       "Random Forest  0.986250\n",
       "Extra Trees    0.976875"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_df = pd.concat([acc_df, pd.DataFrame([extra_accuracy], columns=['Accuracy'], index=['Extra Trees'])], sort=False, axis=0)\n",
    "acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Accuracy of various models')"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(data=acc_df.reset_index(), x='index', y='Accuracy', s=15)\n",
    "plt.xlabel('Model')\n",
    "g.set_xticklabels(rotation=30)\n",
    "plt.title('Accuracy of various models')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### # 1 Ensemble method: voting classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regrouping three different classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf = VotingClassifier(\n",
    "    estimators=[('tree', tree_clf), ('forest', forest_clf), ('extra', extra_clf)],\n",
    "    voting='hard')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the voting classifier to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('tree',\n",
       "                              DecisionTreeClassifier(class_weight=None,\n",
       "                                                     criterion='gini',\n",
       "                                                     max_depth=None,\n",
       "                                                     max_features=None,\n",
       "                                                     max_leaf_nodes=None,\n",
       "                                                     min_impurity_decrease=0.0,\n",
       "                                                     min_impurity_split=None,\n",
       "                                                     min_samples_leaf=1,\n",
       "                                                     min_samples_split=2,\n",
       "                                                     min_weight_fraction_leaf=0.0,\n",
       "                                                     presort=False,\n",
       "                                                     random_state=None,\n",
       "                                                     splitter='best')),\n",
       "                             ('forest',\n",
       "                              RandomForestClassifier(b...\n",
       "                                                     warm_start=False)),\n",
       "                             ('extra',\n",
       "                              ExtraTreeClassifier(class_weight=None,\n",
       "                                                  criterion='gini',\n",
       "                                                  max_depth=None,\n",
       "                                                  max_features='auto',\n",
       "                                                  max_leaf_nodes=None,\n",
       "                                                  min_impurity_decrease=0.0,\n",
       "                                                  min_impurity_split=None,\n",
       "                                                  min_samples_leaf=1,\n",
       "                                                  min_samples_split=2,\n",
       "                                                  min_weight_fraction_leaf=0.0,\n",
       "                                                  random_state=None,\n",
       "                                                  splitter='random'))],\n",
       "                 flatten_transform=True, n_jobs=None, voting='hard',\n",
       "                 weights=None)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = voting_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With the voting classifier using all the previous models, we have reached a 98.812500% accuracy\n"
     ]
    }
   ],
   "source": [
    "voting_accuracy = accuracy_score(y_pred, y_test)\n",
    "print('With the voting classifier using all the previous models, we have reached a' + \n",
    "      ' %f%% accuracy'%(voting_accuracy*1_00))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.982500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.986250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extra Trees</th>\n",
       "      <td>0.976875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voting</th>\n",
       "      <td>0.988125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Accuracy\n",
       "Decision Tree  0.982500\n",
       "Random Forest  0.986250\n",
       "Extra Trees    0.976875\n",
       "Voting         0.988125"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_df = pd.concat([acc_df, pd.DataFrame([voting_accuracy], columns=['Accuracy'], index=['Voting'])], sort=False, axis=0)\n",
    "acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Accuracy of various models')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SUgrDlePkHd9O/rWTkJcN1qWwqlgH6/odsKxctCb2FuJpyNCEKHKMNxLJ2jYH4/UHz6Rl4ViZ0p3fx6J80TjYIsTDyNCEKDaMNxLJXDflL0MYwHj9Cplrp2C8WTQm9hbiaUgQiyJDKUXWtjmo7Eeb2Ftl3yZra9GY2FuIpyFBLIoMw5XjD90T/iPj9SsYrmg/sbcQT0OCWBQZecefbGLvvN+0n9hbiKchQSyKjPxrTzZB95NuJ0RRIUEsio68J5ygO1f7ib2FeBoSxKLosH7CCbpttJ/YW4inIUEsigyrik82QfeTbidEUSFBLIoM6/pPNrG3dT3tJ/YW4mlIEIsiw7JyfSwcH29ibwvHylhW1n5ibyGehgSxKDJ0Oh2lO72PrtSjTeytK1WW0p2LxsTeQjwNCWJRpFg4uGPXM/Che8YWjpXvPE/mmhDPAZn0RxRJptnXfttx5zzh3Gyw+f/Z1+q1l9nXRLGj2cTwkZGRfPPNN+Tn5zNkyBAGDBhQYP2uXbuYMWMGALVr1yYkJIQyZcpw8+ZNPvzwQ/R6PTY2NkyePJm6deuSl5dHixYt8PDwMLWxatUqLC0tH7kmCWIhhBY0mX1Nr9cTGhrK0qVLWbNmDeHh4Zw5c8a0/tatW4wfP57Q0FAiIyPx8vIiNDQUgAULFlC7dm3WrVvHiBEjCAkJAeDkyZM0bdqUtWvXmv57nBAWQoiiyGxBHB0dTcuWLXFwcMDOzg5fX182b95sWp+QkEClSpXw9PQEwMfHh6ioKACMRiMZGRkAZGVlUarUnRP2jx49SlpaGr1796Zv3778+uuv5ipfCCEKjdnu0JGUlISLi4tp2dXVlbi4ONNytWrVSExMJD4+Hi8vLzZt2kRKSgoAw4YN4/XXX6dt27ZkZGTw3//+F7hzVL1Dhw688847nD59moCAACIjI3FycjJXN4QQwuzMFsRGo7HAwRSlVIHlcuXKMXXqVIKCgjAajfTt2xdra2sAJk+ezIABAxg8eDCHDh1izJgxbNiwgX79+pm2r1evHo0aNeLgwYN07NjxketydrZ/Br0TQohnx2xB7O7uTmxsrGk5OTkZV1dX07LBYMDd3Z2IiAgA4uLiTAfhtm/fbhoXbtq0Kc7Ozpw9e5Zz587x4osvUqVKFeBOuN8N70clB+uEEFrQ5GBd69atiYmJIS0tjaysLLZu3Yq3t7dpvU6nY9iwYej1epRSLFy4ED8/PwC8vLxM48UJCQkkJSVRvXp1Tp48aRqmOHfuHCdOnKBZs2bm6oIQQhQKs5++Nm/ePPLy8ujTpw8BAQEEBAQwatQoGjZsyM6dO5k5cya5ubm0atWKwMBArK2tSUhIIDg4mLS0NGxsbPjwww9p3bo16enpTJw4kXPnzqHT6QgMDKRly5aPVZPsEQshtKDZecRFkQSxEEILchdnIYQowiSIhRBCYxLEQgihMQliIYTQmASxEEJoTIJYCCE0JkEshBAakyAWQgiNSRALIYTGJIiFEEJjEsRCCKExs02DKe6nlOJ4Qho/H7zCyYs3yM41UMrGkjpVHPB5sTL1qznJDTGFKIFk0p9CkpiWyderjnIlJeOBz6lcoQzv9W6Im5NdIVYmhCgMMumPxhLTMvl8yYG/DGGAKykZTFlyAH1aZiFVJoQoCiSIzUwpxderjpKelfdIz0/PyuM/q45Swr6oCFGiSRCb2fGEtIfuCf/RlZQMfku4bqaKhBBFjQSxmf188MoTbbfj4OVnXIkQoqiSIDazkxdvPNF2py492XZCiOJHgtjMsnMNhbqdEKL4kSA2s1I2loW6nRCi+JEgNrM6VRyeaLvaHk+2nRCi+JEgNjOfFys/0XbtX3zhGVcihCiqJIjNrH41JypXKPNY21SuUIZ61RzNVJEQoqiRIDYznU7HyN4NsS9t/UjPL2tnzXu9G8qcE0KUIBLEhcDdyY7AQc0eumdcuUIZJg5sJnNNCFHCyKQ/hUgpxW8J19lx8DKnLv0++1ptDwfav/gC9ao5yp6wEM+pv5r0R4JYCCEKgcy+JoQQRZgEsRBCaEyCWAghNCZBLIQQGpMgFkIIjUkQCyGExiSIhRBCYxLEQgihMQliIYTQmASxEEJoTIJYCCE0ZtYgjoyMxM/Pj86dOxMWFnbf+l27duHv74+/vz9jx44lI+PObedv3rxJQEAAPXr0oE+fPpw4cQK4M2nO1KlT6dKlC35+fhw4cMCc5QshRKEwWxDr9XpCQ0NZunQpa9asITw8nDNnzpjW37p1i/HjxxMaGkpkZCReXl6EhoYCsGDBAmrXrs26desYMWIEISEhAGzZsoWzZ8+yceNGvv76ayZMmEB+fr65uiCEEIXCbEEcHR1Ny5YtcXBwwM7ODl9fXzZv3mxan5CQQKVKlfD09ATAx8eHqKgoAIxGo2nvOCsri1KlSgF39qD9/PywsLCgevXqVKxYkUOHDpmrC0IIUSiszNVwUlISLi4upmVXV1fi4uJMy9WqVSMxMZH4+Hi8vLzYtGkTKSkpAAwbNozXX3+dtm3bkpGRwX//+19Tm66urqY2XFxcSExMNFcXhBCiUJgtiI1GY4FJzpVSBZbLlSvH1KlTCQoKwmg00rdvX6yt79xOaPLkyQwYMIDBgwdz6NAhxowZw4YNG/60TQuLx9upd3a2f8qeCSHEs2W2IHZ3dyc2Nta0nJycXGBv1mAw4O7uTkREBABxcXF4eHgAsH37dtO4cNOmTXF2dubs2bO4u7uTlJRkaiMlJaVAm49CJoYXQmhBk4nhW7duTUxMDGlpaWRlZbF161a8vb1N63U6HcOGDUOv16OUYuHChfj5+QHg5eVlGi9OSEggKSmJ6tWr4+3tTWRkJAaDgQsXLpCQkEDDhg3N1QUhhCgUZr1VUmRkJPPmzSMvL48+ffoQEBBAQEAAo0aNomHDhuzcuZOZM2eSm5tLq1atCAwMxNramoSEBIKDg0lLS8PGxoYPP/yQ1q1bo5Ri2rRp7N69G4AJEybQtm3bx6pJ9oiFEFqQe9bdQ4JYCKEFuWedEEIUYRLEQgihMQliIYTQmASxEEJoTIJYCCE0JkEshBAakyAWQgiNSRALIYTGHhrE169fL4w6hBCixHpoEHfr1o2xY8cWmMBHCCHEs/PQS5yzs7PZsGED4eHhZGVl0a9fP3r27Im9ffGcTlIucRZCaOGZzTWxb98+Jk6cSFpaGr169WLUqFE4Ojo+kyILiwSxEEILTz3XxO7du3n//fcZM2YMHTt2ZPny5VSsWJERI0Y8syKFEKKkeujE8D4+Pjg4OPDGG28wffp00/3j6tSpQ3h4uNkLFEKI591DhyYOHjxInTp1KFOmDLm5udy+fRtnZ+fCqu+Zk6EJIYQWnmpoIjExkddeew2AK1eu0K1bN3bs2PHsqhNCiBLuoUH87bffsnjxYgCqV6/O6tWrmTNnjtkLE0KIkuKhY8RGoxF3d3fTcsWKFTEajWYtSoiSTilFfNppdl+J4fSNc+QYcrC1tKWWQw28K7fCy6lWgTuai+LtoUHs5OTE8uXL6dOnDzqdjtWrV1OhQoXCqE2IEkmfmcz8o4u5lqEv8HhWfhZxKceJSzlOxTJuvN1wMK52LhpVKZ6lhx6sS0hI4IMPPiA+Ph6dTkf9+vWZMWMGVapUKawanyk5WCeKMn1mMrMOzCU9L+Ohz7W3LsPYZiMkjIuJZ3JBx82bN7G0tCy2V9TdJUEsiiqlFFN+nXXfnvBfqVjGjcCXP5BhimLgr4L4oUMTaWlprFu3joyMDJRSGI1GLly4wMyZM59pkUKUdPFppx8rhAGuZeiJv36auk61zVSVKAwPDeLRo0dTqlQpzpw5Q+vWrYmOjqZZs2aFUZsQJcruKzFPtN3/LsdIEBdzDz197erVq3z33Xd4e3szcOBAli1bxrlz5wqjNiFKlNM3nuzv6km3E0XHQ4P47hkS1apV49SpU7i5uZGfn2/2woQoaXIMOU+0XfYTbieKjocOTTg7O/P999/TpEkT5syZg729PdnZ2YVRmxAliq2lLVn5WY+9XSlLWzNUIwrTQ/eIQ0JCsLGxoXnz5jRo0IDZs2fz4YcfFkZtQpQotRxqFOp2ouh46Olr48aNY9q0aYVVj9nJ6WuiqDqReor/HPn+sbd7r8lwOVhXDDzVpD8nTpzgMeaOF0I8IS+nWlQs4/ZY21Qs44aXYy0zVSQKy0PHiF1dXenWrRuNGzemTJkypsc/+eQTsxYmREmj0+kIaDj4sa6se7vhYLmY4znw0CBu2rQpTZs2LYxahCjx3OxcGNtsBN/9yVwT95K5Jp4vj3XPuueBjBGL4kApRfz10/zv8p3Z17INOZT6/9nXXnmhFV6OMvtacfNUc034+/v/6eORkZFPV5VGJIiFEFp4qrkmgoKCTD/n5eWxYcMGPDw8nk1lQgghHn9oQilFv379iu2NQ2WPWAihhac6fe2Prl+/TlJS0lMVJIQQ4ncPHZr44xjx1atXef31181WkBBClDQPHZr49ddff3+yToeTkxM1a9Y0e2HmIkMTQggtPNXQRJUqVdi4cSMvv/wyzs7OzJw5k5SUlEf6xZGRkfj5+dG5c2fCwsLuW79r1y78/f3x9/dn7NixZGTcOYm9d+/e9OzZk549e+Lr60u9evVISUnhypUrNG3a1LTurbfeeqQ6hBCiKHvoHvHQoUNp3749gwcPJicnh2XLlvHLL78wf/78v2xYr9fTv39/Vq1ahY2NDf369WPWrFl4enoCcOvWLXx9fVmyZAmenp7Mnz8fvV5/3xV748aNo0aNGrz77rts2bKFX375hZCQkCfusOwRCyG08FR7xNevX2fw4MEA2NraMnToUJKTkx/6S6Ojo2nZsiUODg7Y2dnh6+vL5s2bTesTEhKoVKmSKZh9fHyIiooq0EZMTAzx8fEEBAQAcPToUU6dOkXPnj0ZPHgwJ0+efGgdQghR1D00iA0GA3r975dapqSkPNIkQElJSbi4/H75paura4F2qlWrRmJiIvHx8QBs2rTpviGP2bNnM2bMGCwtLYE7HwQ9evRg9erVvPXWW4wcOZLc3NyH1iKEEEXZQ8+aGDp0KL169eKVV15Bp9MRHR3NuHHjHtqw0WgscAmmUqrAcrly5Zg6dSpBQUEYjUb69u2LtbW1af3p06e5fv06Pj4+psfef/9908/t2rVj5syZnDt3Di8vr4f39P85Oxfvu1ALIZ4/Dw3iPn360KBBA/bu3YulpSXDhw+nVq2HT7vn7u5ObGysaTk5ORlXV1fTssFgwN3dnYiICADi4uIKXLEXFRWFn59fgTaXLFlC9+7dcXR0BO6Eu5XVQ7tQgIwRCyG08FRjxHq9nuXLlzN06FDatGlDaGjoI40Rt27dmpiYGNLS0sjKymLr1q14e3ub1ut0OoYNG4Zer0cpxcKFCwsE7+HDh2nevHmBNvfv38/KlSuBO6fVGY1GatSQuxMIIYq3hwbxxx9/bAq7ypUr8/LLLzNx4sSHNuzm5saYMWMYPHgwvXr1onv37jRq1IiAgACOHj2KhYUFISEhDB8+nC5dulCuXLkCp6NdunQJN7eCk2QHBgYSHR1N9+7dmTp1KjNnzsTC4rEvDhRCiCLloaev9ezZk7Vr1xZ4rFevXqxZs8ashZmLDE0IIbTwVEMTT3rWhBBCiEfzWGdNwJ1zex/lrAkhhBCP5pGmwYyPjzedNXHz5k127dplOtuhuJGhCSGEFp5qYniAihUrkpubS1hYGJmZmQwaNOiZFSeEECXdXwbxuXPnWLRoEevWraNy5cpkZ2ezY8cOypZ9cLILIYR4PA88WPf2228zcOBArK2tWbx4MevXr6dMmTISwkII8Yw9MIh/++036tevT61atahatSqA3DVWCCHM4IFBvHPnTl577TXWr19P27ZtGTVqFDk5OYVZmxBClAgPDGIrKyv8/PxYsmQJq1atwtXVlZycHDp37syyZcsKs0YhhHiuPdZdnLOysli3bh3Lly9n9erV5qzLbOT0NSGEFv7q9LXHCuLngQSxEEILT3WJsxBCCPOSIBZCCI1JEAshhMYkiIUQQmMSxEIIoTEJYiGE0JgEsRBCaEyCWAghNCZBLIQQGpMgFkIIjUkQCyGExiSIhepUkW0AACAASURBVBBCYxLEQgihMQliIYTQmASxEEJoTIJYCCE0JkEshBAakyAWQgiNSRALIYTGJIiFEEJjEsRCCKExCWIhhNCYBLEQQmhMglgIITQmQSyEEBqTIBZCCI1JEAshhMaszNl4ZGQk33zzDfn5+QwZMoQBAwYUWL9r1y5mzJgBQO3atQkJCaFMmTL07t0bg8EAQHZ2NpcuXWL37t2UK1eOwMBAjh07RqlSpZgxYwY1a9Y0ZxeEEMLsdEopZY6G9Xo9/fv3Z9WqVdjY2NCvXz9mzZqFp6cnALdu3cLX15clS5bg6enJ/Pnz0ev1fPLJJwXaGTduHDVq1ODdd9/lhx9+4MKFC4SEhLB//36mT5/OihUrHquu1NR0jEazdFkIIR7IxaXsA9eZbWgiOjqali1b4uDggJ2dHb6+vmzevNm0PiEhgUqVKpmC2cfHh6ioqAJtxMTEEB8fT0BAAAA7d+6kR48eALz00kukpaVx9epVc3VBCCEKhdmCOCkpCRcXF9Oyq6srer3etFytWjUSExOJj48HYNOmTaSkpBRoY/bs2YwZMwZLS8s/bdPFxYXExERzdUEIIQqF2caIjUYjOp3OtKyUKrBcrlw5pk6dSlBQEEajkb59+2JtbW1af/r0aa5fv46Pj88D21BKYWHxeJ8lzs72T9IdIYQwG7MFsbu7O7Gxsabl5ORkXF1dTcsGgwF3d3ciIiIAiIuLw8PDw7Q+KioKPz+/Am26ubmRlJRElSpVAEhJSSnQ5qOQMWIhhBY0GSNu3bo1MTExpKWlkZWVxdatW/H29jat1+l0DBs2DL1ej1KKhQsXFgjew4cP07x58wJttmvXjrVr1wIQGxuLra0tlSpVMlcXhBCiUJgtiN3c3BgzZgyDBw+mV69edO/enUaNGhEQEMDRo0exsLAgJCSE4cOH06VLF8qVK8dbb71l2v7SpUu4ubkVaHPQoEHk5ubSrVs3pkyZwrRp08xVvhBCFBqznb5WVMnQhBBCC5oMTQghhHg0EsRCCKExCWIhhNCYBLEQQmhMglgIITQmQSyEEBqTIBZCCI1JEAshhMYkiIUQQmMSxEIIoTEJYiGE0JgEsRBCaEyCWAghNCZBLIQQGpMgFkIIjUkQCyGExiSIhRBCYxLEQgihMQliIYTQmASxEEJoTIJYCCE0JkEshBAakyAWQgiNSRALIYTGJIiFEEJjEsRCCKExCWIhhNCYBLEQQmhMglgIITQmQSyEEBqTIBZCCI1JEAshhMYkiIUQQmMSxEIIoTEJYiGE0JgEsRBCaEyCWAghNCZBLIQQGjNrEEdGRuLn50fnzp0JCwu7b/2uXbvw9/fH39+fsWPHkpGRAUB6ejpjx46lV69e9OrVi+PHjwNw5coVmjZtSs+ePenZsydvvfWWOcsXQohCoVNKKXM0rNfr6d+/P6tWrcLGxoZ+/foxa9YsPD09Abh16xa+vr4sWbIET09P5s+fj16v55NPPiEwMBBHR0c+/PBDdu/ezZw5c4iIiGDLli388ssvhISEPHFdqanpGI1m6bIQQjyQi0vZB64z2x5xdHQ0LVu2xMHBATs7O3x9fdm8ebNpfUJCApUqVTIFs4+PD1FRUSil2Lp1K2+//TYA3t7efP755wAcPXqUU6dO0bNnTwYPHszJkyfNVb4QQhQaswVxUlISLi4upmVXV1f0er1puVq1aiQmJhIfHw/Apk2bSElJITU1FRsbG5YuXcrrr7/O4MGDMRgMANja2tKjRw9Wr17NW2+9xciRI8nNzTVXF4QQolBYmatho9GITqczLSulCiyXK1eOqVOnEhQUhNFopG/fvlhbW2MwGEhJSaFs2bKEh4fzyy+/MHLkSLZv3877779v2r5du3bMnDmTc+fO4eXl9ch1OTvbP5sOCiHEM2K2IHZ3dyc2Nta0nJycjKurq2nZYDDg7u5OREQEAHFxcXh4eODo6IiVlRXdu3cHoE2bNmRmZpKamsrGjRvp3r07jo6OwJ1wt7J6vC7IGLEQQguajBG3bt2amJgY0tLSyMrKYuvWrXh7e5vW63Q6hg0bhl6vRynFwoUL8fPzw8bGhtatW7NhwwYADh8+TOnSpXF0dGT//v2sXLkSgF9//RWj0UiNGjXM1QUhhCgUZjtrAu6cvjZv3jzy8vLo06cPAQEBBAQEMGrUKBo2bMjOnTuZOXMmubm5tGrVisDAQKytrUlKSiI4OJjLly9jZWXFpEmTaNy4MXq9nvHjx5OcnIytrS1Tpkx5rGEJkD1iIYQ2/mqP2KxBXBRJEAshtKDJ0IQQQohHI0EshBAakyAWQgiNSRALIYTGJIiFEEJjEsRCCKExCWIhhNCYBLEQQmhMglgIITQmQSyEEBqTIBZCCI1JEAshhMYkiIUQQmMSxEIIoTEJYiGE0JgEsRBCaEyCWAghNCZBLIQQGpMgFkIIjUkQCyGExiSIhRBCYxLEQgihMQliIYTQmASxEEJoTIJYCCE0JkEshBAakyAWQgiNSRALIYTGJIiFEEJjEsRCCKExCWIhhNCYBLEQQmhMglgIITQmQSyEEBqTIBZCCI1JEAshhMYkiIUQQmMSxEIIoTGzBnFkZCR+fn507tyZsLCw+9bv2rULf39//P39GTt2LBkZGQCkp6czduxYevXqRa9evTh+/DgAubm5fPTRR3Tt2pXXXnuNs2fPmrN8IYQoFGYLYr1eT2hoKEuXLmXNmjWEh4dz5swZ0/pbt24xfvx4QkNDiYyMxMvLi9DQUAC++OILKlasyJo1a/jggw/417/+BcCSJUsoXbo0mzZtYuLEiUyYMMFc5QshRKExWxBHR0fTsmVLHBwcsLOzw9fXl82bN5vWJyQkUKlSJTw9PQHw8fEhKioKpRRbt27l7bffBsDb25vPP/8cgJ07d9KjRw8AXnrpJdLS0rh69aq5uiCEEIXCylwNJyUl4eLiYlp2dXUlLi7OtFytWjUSExOJj4/Hy8uLTZs2kZKSQmpqKjY2NixdupSff/4ZW1tbJk6c+Kdturi4kJiYSKVKlR65LgsL3TPonRBCPDtmC2Kj0YhO93voKaUKLJcrV46pU6cSFBSE0Wikb9++WFtbYzAYSElJoWzZsoSHh/PLL78wcuRItm/ffl8bSiksLB5vp97RsczTd04IIZ4hswWxu7s7sbGxpuXk5GRcXV1NywaDAXd3dyIiIgCIi4vDw8MDR0dHrKys6N69OwBt2rQhMzOT1NRU3NzcSEpKokqVKgCkpKQUaFMIIYojs40Rt27dmpiYGNLS0sjKymLr1q14e3ub1ut0OoYNG4Zer0cpxcKFC/Hz88PGxobWrVuzYcMGAA4fPkzp0qVxdHSkXbt2rF27FoDY2FhsbW0fa1hCCCGKIp1SSpmr8cjISObNm0deXh59+vQhICCAgIAARo0aRcOGDdm5cyczZ84kNzeXVq1aERgYiLW1NUlJSQQHB3P58mWsrKyYNGkSjRs3Jicnh+DgYI4dO4aNjQ2fffYZ9evXN1f5QghRKMwaxEIIIR5OrqwTQgiNSRALIYTGJIiFEEJjEsRCCKExCWIh7nHr1i3gzgVJQhQWCeLnmMFg0LqEYuXXX39l/vz5AI99xebzQD58tFPy3m0lwN0zEi0tLcnPz+fmzZsaV1S03X29HB0duXTpEkFBQezbt0/jqgqP0Wh8oukCirOi9qFTcl75EuTufBxhYWG88cYbzJw5k61bt3L79m2NKyta7v4x3n29LCws2LVrF0eOHKF27dpallaoLCws0Ol0HDlyhJkzZxaYmuB5ZDQaTR86Fy5cKDArpFYkiJ8DSqn7PuF/+uknDh8+zIoVK3BwcODf//438fHxGlVYNN39Y9yzZw+LFi2ibNmyzJ8/n1atWrFt2zaNqzOve98vRqOR+fPnM3nyZOzt7dm+fTv5+fkaVmdeFhYWJCYm8tlnnxEaGsqKFSu4fPky8Pu3o0KvSZPfKp6Zu7PcWVhYkJWVRW5uLgB5eXl4eHgwY8YM9u7dS2BgILVq1TKtL6nuDaCMjAyCgoKYP38+SUlJREZG0qxZM2rVqsW+ffuey7mu7/b/3mGIrKwsoqOj+e677+jZsyft2rVj//79z82Q1h+PlaSlpTF27Fjc3Nzo1KkTjo6OrFy5EqDA7I6FyfJfd29/IYqVuwF8940zbdo0lixZQnx8PFWrVuXQoUNs3LiRunXrMmPGDNzc3Ojfvz/t27enXLlyGldf+AwGg+kr+N3pVC9fvkxkZCSLFi3Czc0Nd3d3bt++TeXKlbl48SJHjhxBKUXVqlW1Lv+Zuft+2bZtG7NmzSIrK4sXXniBxMREJk2axL59+7hy5QpLliwhLy+PBg0aYG1trXHVT+aPHzq3bt3C1taWU6dOERUVxbRp06hduzZVqlQhJiaG8uXLU6lSpfum2y0MZpsGU5hPfn4+Vla//9MFBwdTrlw5fvjhB3r27ElGRgZt2rShatWq1KxZE7jzh1etWjUcHBy0KltTlpaWAERERBAXF0fHjh2pXbs2pUqVol27dtStWxeDwcDJkycJCQmhY8eOTJ8+HVtbW1q1alXg9S5u/hgsX331FSdOnOCdd95h9erVrFixghUrVvDuu++Sk5ODo6Mj4eHhJCcnY2dnp2HlT+b69evY29ubPkBOnz7NF198gYWFBf3796dBgwY4OjoSGxtL8+bNcXNzIz09nW3btvHiiy9qctCy+L67SpjffvuNixcv0qVLF6ysrDh+/DhLlixhwIABVKxYEQ8PDyZMmICTkxNDhw6lVq1aKKXYsmULYWFhGAwGAgMDKVOmZEyMr5QqcCZATk4OEyZMwNraGj8/PxYvXky9evX4+uuvSUtLw8bGhvLlyzNp0iQyMzN59dVX+e6777C3t9e4J0/OYDBgaWlZ4FtAVlYWN27c4MsvvyQiIoKTJ0/y+uuvk56ezpkzZ5g2bRqNGzdm//79jBw5UusuPLYDBw4QGxvLgAEDsLCwYN68eZw9e5Z+/fpx+fJloqKiuHHjBt7e3vz44480b97c9G+clJTEvn37aNWqVaHXLUFcxF2/fp3Q0FBOnDhh+sPYuHEj06dPJzg4mJo1azJjxgw2b97M0KFDee2119ixYwdffvklP/zwA506deL8+fPUqVNH454UrrvDNteuXePAgQPUrVuX8uXL8+mnn/L555+Tm5tL69atycrKYt++fezZswdbW1vOnTtH//79AYp1CMPv3wLCwsI4ffo07du3p02bNpw5c4aOHTsycOBAfvjhB27fvs2HH37IrFmz6Nu3LykpKSxevLhY7Q0fP36c3NxcmjVrRrNmzTh79izly5fn1KlTJCYm0rlzZ65fv05YWBgXL17Ez8+P2NhYRo8ezdGjRxk4cCDnz5/XbBhGDtYVYd9//z1dunTh2LFjRERE8Oqrr2IwGLCxseH27dt4enpiZ2dHixYt8PDwoHnz5gDs37+fli1bAmBjY1NiQvjumODd/0dERDBx4kRSU1NxdnZm27ZtdOjQgQoVKrBw4UKUUsybN48mTZpQpUoVqlatypIlS4rtqWtKqQIHpjIzMxkxYgQnT57Ex8eHefPmMXv2bAYPHkyVKlUYPXo09vb2rFmzhpo1a2JnZ0evXr0YPnx4sQphpRSxsbEcOnSI2NhY047K6dOnGTlyJBcvXuTs2bM4OjrSuHFjbt68yalTp5gzZw49e/bkxx9/pGPHjiQkJFCqVClN+iAH64qg69ev88UXX5CUlERAQAB79uzBx8eHsmXLYmFhgaurK9evX2fv3r106NCBF198kSNHjrB+/XoWLlxITk4OI0eOLFZ/TE/jjwdldDodRqORL7/8kvLly/PRRx9hbW1tut3WRx99hIWFBdOnT6dVq1Y0a9aM5s2b8+KLL2rZjSem1+vJz8+ndOnSWFhYcOHCBfbu3Ws6IBkcHMx///tfMjIy6NevHw0aNCAuLo7ly5ezfv16Tp8+zcCBA4vd3W7uPQCblpbG5MmTOXPmDAEBAVy4cIFr167RokULjEYja9aswd/fnxdeeIGjR4+SmZlJgwYNOHbsGAsWLCAiIoL33nuPl156SZvOKFHk5ObmqitXrpiWJ0yYoD766KMCzzly5Ih688031Z49e5RSSmVnZ6vExER14sSJQq1VS0lJSQWWjxw5okaNGqXmzZunDh06pE6cOKEGDhyojh8/roxGozpx4oTy9/dX48aNU4MGDVLDhw9XFy9e1Kj6p5ebm6tmzZqlfH191ZEjR5TRaFRhYWFq4MCBasWKFSoxMVG1aNFCderUSS1YsEAZjUa1bds29f3336v8/Hx14sQJFRkZqXU3Hkt+fn6B5ZycHGU0GtWxY8fU+PHj1ZQpU1RGRoY6ceKEGjNmjNq8ebNKSkpS3bp1U+vWrVNKKZWamlqgjaNHjxZa/Q8iQxNFkLW1NZUqVTJ9zRw3bhwHDx4kJibG9BxPT0/atGnD3LlzAbC1tcXNzQ0vLy9Nai5MaWlpBAcHM2HCBLKysoA74+aBgYG0atWK8uXL8+677+Lo6Ej16tXZvHkzGRkZeHl58e2339K9e3f69evH/Pnz8fDw0Lg3T2bVqlX4+vpy4MABNm3aRKNGjcjLy2PVqlW4u7vz97//nbJly9KtWzdeeeUVhg4dik6n46effsLT0xNLS0u8vLxMN+ktDi5dukTPnj1NF138+OOP9OvXj0mTJhEdHU1ISAipqalERkbi5eVFw4YN2b9/P7dv32bo0KFcu3YNACcnJwDTRSsNGjTQpkP3kKEJjd09H/jPWFhYYDAYsLOzIycnh59++olu3bphaWmJtbU1Dg4OlC9fHi8vrxIzT8D8+fP597//jaenJ5999hm2trYopVixYgUDBgygW7duNGjQgOTkZLZt28bo0aNZuHAhL7zwAh4eHpQrV46qVatSq1Ytrbvy2JRS6PV63nrrLS5fvkyzZs1wdHSkZcuWWFlZYWlpSbly5Thw4AD169fH1dUVZ2dnvv/+e06ePMn3339P6dKl6dOnT7E8EFm2bFmioqKoUaMGhw8fZv369QQFBeHl5cXkyZNp3LgxLi4uHDhwgHr16uHu7k5UVBQeHh506dLFdAzlriL1N6PxHnmJZjAYTD8fP35c3bhxw7RsNBoL/F8ppf72t7+pxYsXF16BRcjt27fVhAkT1Kuvvlrgq+XZs2eVUkp99NFHasqUKabHk5OTlbe3t8rJyVGff/65CgsLK/B6F2c7duxQSil16dIl9emnn6offvihwPp//vOfau7cuSojI0MppdSFCxfUzp071ebNmwu91qeRkZGhcnNzTct6vV6NHTtWHTt2TH3wwQdq48aNpnU//fSTeu2111ReXp6aPHmy6t69u1q8eLE6d+5cgTaL6ntA9og1pNPpSEpK4ssvv2T+/PkcP34cKysratasadpL1ul0poMS9vb2bNmyha5du5pOTSopdDodN2/epFSpUvj6+nLlyhVGjx7NmTNnaNu2LW5ubqxatYoGDRpQoUIFYmNjycjIoGvXrrRs2ZImTZpodvnqs6L+/1zg6tWrA3eGozIzM9m/fz81atTA2dkZADc3NyIiIqhatSqVK1fGwcGBatWq4enpqWX5j+XatWuMGTOG3NxcGjZsCECZMmVYsWIFLi4uuLq6snPnTrp27QpA1apVWblyJZ06deKll16iXr16dO/eHUdHR+D3166ovgckiAvR3UC9KzY2ljFjxtCpUyemTZvGqVOnOH/+PJUrV8bJycn05rm7Ta1atejRo0eJC2G4c06snZ0dp0+fZvr06Wzfvh1/f3/++c9/YmVlhZubG5cvX2bhwoUcOnSIdevW0aNHD9OwTVH9A/wrf5wd7t4+KKWwsrLCzs6OCxcucOrUKVq3bg1AxYoVOXToEPb29nh5eRXLvpctWxY7OzuWL19OWloajRo1Mr3v756Ot2nTJtNY99KlS8nJyaF3797Y2dmZxv6LegDfJUFcSNQ9V3ldunSJ8uXLY21tzaJFi2jatCmNGzemTJkyxMfHc+vWLRo3blzk3zzm8scPrLscHR3Jz88nLi6OESNGmA403b2C7O6paKVKlWLcuHGmPani+Dree1XcrVu3sLCwwNLSssApWwDly5cnLy+Pw4cPY2NjQ7Vq1QBo27Yt9evXL5Z9v8vT05PatWsTERHBhQsXqFOnDhcvXiQ1NZUePXpQqlQpli1bxtq1a7l8+TIjR47E1dW1QBvFpf86pTSa960ESEhIwMnJyTTJTmxsLKGhoZQqVYoaNWrQtWtXEhMTmTt3LuvXrwfuXIRw8OBBevToocmllkVJWloaDg4OWFhYmObXSElJYeXKlaSkpDBu3DhsbGw0maTFXO7ty82bN5k8eTJ6vZ4qVaowZcqUP31uamoqixcvJicnh3HjxhWtg1DPQHx8PD/99BNpaWkMGTKE4OBgli9fTqlSpcjIyODKlSumi3CK63vh+foXK0LS09MJCwszfb1MTEzkm2++4e2332b69Ok4OTnx3XffUadOHapWrcq3334LYLpKrkKFClqWX+juvSIsJSWFN998k9GjRzNz5kzg9yPcFSpUoFmzZmRlZbFx40ZNajWHPw5D6PV6hg8fTt26dfnqq69YvXq16cP67mlXd5/r7OxM3759n8sQBvDy8iIwMBBLS0s2bNhAdnY2ycnJwJ1x47shbDAYimUIg+wRP3N//Oq4Z88ePD09ycjIYOLEiSxZsgQbGxvS0tKYO3cuNWvWpHbt2gwfPpwdO3aYDi6UFPfeLSEvL4/9+/dz/fp1bt68ibe3N76+vixatIjmzZuTl5eHtbU16enpLFu2jPPnzxMcHKzZZanPws2bNylfvrxpeceOHRw5coR69epx/vx5+vXrx4YNG9i9ezcHDx5k//79GlarjbvvkfT0dH799Vd++uknZs2aha2trdalPTMyRvwM3Tuul5mZibW1tWkvpWzZsty4cQNnZ2fc3d0pXbo0mzZtwsPDgw4dOlC2bFnq1q2LjY1Nsf1UfxJ3+3r48GFGjx7N3r172bFjB3379qVu3bpYW1szZ84cBg0ahKWlJUopbG1tqVy5Mr6+vsU6hA8ePEhERARt2rQhOzubsLAwvv32W8aPH4+Liwuenp7873//49atWwQHBxMeHs7Vq1d55ZVXtC69UN19j9jY2FC9enW6detWrKcl/TPP3/eYQnb58mXOnj0L3Dmyf+zYMd566y2mT5/OsWPH+OCDD0xXxFlaWrJ+/XpOnTqFXq/n4sWLpoMLAwYMoGzZss99CKs/ua3TtGnTmD17NiNHjiQ8PJwOHTqwc+dOAN5++23y8/NZuHAh8PsQhru7e7EN4bt9cHBwQK/XExwczObNm0lOTqZ06dJ4eHjg4eFBZmYm69ev54033iA1NZXq1asTHR2NXq/XuAfae97uUC5B/BTy8/PZuHGjKRDi4uIIDg5myJAh+Pv7Y2try8svv0yFChU4cOAA3bp1o0KFCkybNo3hw4fTo0ePErV3c+9tnVJSUoiLiwPA39+f69evk5+fj4WFBe3atSM5OZmoqCgAPv74Y5YuXQrwXOwJ3T0NSynF7t27OXDgAK+88gq9e/emWrVqrFq1CrhznnBcXBxhYWEMHDiQdu3aERERgZubm5blFwnP2ymcMkb8BO4eFLg7thkbG0teXh42NjasW7eOJk2akJSURHZ2NqVLl6Znz558+OGHDB8+nHbt2nHt2jVcXV2fuzfTg2zbto169epRuXJljEYjc+fOZevWrdSqVYvz58/zzTffsGjRIpRSvP/++xiNRpYvX05cXByff/55sbwc916ZmZmmmfDujne++eabDB06lDJlypgu2+3bty9hYWEcPXqU0aNH4+7uzo4dOzh9+rTpdDTxfJI94seklMLS0hILCwvTJCKRkZHs27eP7OxsXF1dWb9+PUopXFxc+Prrr0lPT+fFF1/k/PnzKKWoWLFiiQnhc+fO8fPPP5v28hISEoiPj2fhwoXMnDmTFi1a8MknnzBo0CCOHTvG/v37sbe3p3nz5rRt29Y0LlwcXb58maCgILZu3QrA1atX2bdvHwCVKlWiTJkyNG/eHC8vL2JiYkhMTKRLly7Y2NgQFhYGQPv27XnnnXckhJ9zxf97XiHT6XScP3+eSZMmkZuby0svvUTXrl1ZvXo1N27cYOTIkeTm5pKRkYGjoyN79+7FxsaGf/7zn8/lqUUPcnfPr0aNGjRt2pRffvmFs2fPEh8fz82bN00zYH388ce0bduW1NRUOnbsSFhYGI0bN6ZJkyY0adJE4148GYPBwH/+8x927dpFhw4d6NWrFwBbt27lp59+om7duqaJygGaNm3KiRMnWLJkCc2aNeOll14qkTd4LcnkrImH+ONVXunp6cyYMYO2bdsyceJEsrKyaNeuHRcvXuT06dMopdi4cSNff/01P/74I+3bt6ddu3bP/UG4P9LpdNy+fZt//etfHDlyhGvXrpGbm0vnzp3ZuHEj9evXx8XFBYCzZ8/i4eFB586dqVChQrG+o8jOnTsZPHgwtWrV4pNPPsHHxwe4c15w27Ztad++Pbdu3WLVqlUkJSVRqVIlvLy8cHR0ZO3atQD069fPdNNXUTLIGPEjunDhAlWrViU9PZ033niDadOmmeb+NRgMHD16lPDwcOrUqcPf/vY3Tp48Sb169UrUXTLu/cBSSrFo0SIuXLjAp59+yuLFi/ntt99o1aoVmZmZhIeH880337Bp0yYiIyOZPXt2sZ0b+F6bNm0iODjYdL7v/v37TR/cI0aMMA1JDRkyhFq1anHp0iUAgoKCcHJyKjHvF1GQDE38iXsvk4yOjmb27NlUrlyZOnXqUK9ePXx8fDhy5IgpiENCQmjatCktWrQgMTERW1vb++Y+fV6pP9wt+ejRo1SvXh17e3sOHjxomnTb398fe3t79u/fT1BQENeuXWPmzJnktnzdAgAACAJJREFU5OQ8NyEM0LVrVzZt2kRgYCClS5fm6NGjDBo0qMAE7PHx8ZQrV45PPvmEGzduEBcXxwsvvKBh1UJrEsT/78qVK8TFxeHl5WWaOOXs2bMsX76cGTNmkJKSwv+1d28hUa57HMe/aS4P5GBOauWFiGgH1KwMifAAUqZJU6Y1dtLSkJJGKjpYQlEY6UUkhhTYSQyy0tLKQiyEULLIlIaOlJGjNZWCoZky2bpo++5qtdZem72Xb/b+P3fzzug8z4U/Hv/PafPmzSxfvhwPDw+amprQ6XTExMTQ0dFBVFSU8m+olnx9W/KhQ4e4e/cuwcHBJCQkMG/ePOrr6+np6WHs2LE4ODjQ0tLChQsX2Lx5M/39/b/U7qghGzZsIDk5mUWLFlFWVvaH9202G+3t7XR1deHu7k5ERIQKrRQ/E+3MHv2JT58+UVBQQFpaGjdu3GDbtm1s2rQJm82m7Om/fPky+/btIzU1ldWrVxMWFkZ0dDSVlZUkJiYSEhKiqRD+fjF9Q0MD6enpzJ49m9raWry9vWlsbGRwcBAXFxfy8/MBMJvNTJ8+XRn9/YohDF/ORjAajXR3d3/z/OrVq2zZsoXOzk5OnjypTFgKoekRcV1dHTk5OSxZsoSzZ8+i0+l48+YNq1at4uTJk/j7+9PZ2cmDBw84deoUY8aMwWQyERUVRUJCAqGhoTg5OWmmrjdUshmqc5rNZgIDA/Hx8cFqtSrlnMjISGpqaujp6SEtLY28vDyMRiMBAQHs2rXrlw3gr6Wnp5OamkpzczMTJkxgz549fPjwgY0bN2qmbCX+Pk0HcV9fHwMDA2zatAmA3t5ePD09MZlMXLx4kZCQEPz9/XFxcaG3t5d3795htVqVGxK0MqIZOoJyKGhv3bpFfn4+g4ODREREkJKSQlZWFiUlJRgMBkJDQ3n8+DEtLS2EhIRQUFDA+/fvNbUkS6/XYzQaMRqNBAUFsWzZMhITE9VulvhJaXr5mr+/P/fv36e5uZnw8HDs7Oyws7MjICCAkpISvL29SUpK4t69e5w5c4by8nJSUlKIiopSu+nDoq2tjdzcXO7evcvEiRNxd3fn9u3bFBUVsX//fubMmcPhw4fx9PRk/vz51NbW0tfXR3BwMC4uLnR2djJ16lTc3Nw0MQr+3uTJkxk3bhw5OTkEBwer3RzxE9P88rWHDx+SmZnJsWPH8PX1paenhzFjxlBYWEhbW5tS32xvb8fb21vl1g6PoQ0JdXV1zJ07lydPnvD8+XOqqqpobGzk+vXrrFixgoqKClpbW9HpdMrNwllZWdTX1+Ps7Kx2N4QYMTQ/WTdlyhRiYmLIy8sDUM41sFqtzJs3T/mcVkK4rq6OyMhI+vv7OXLkCBs2bCAjIwO9Xs/Hjx8JCAggMzOTa9euERQURHZ2Ng0NDZSWluLn50d2djbAiN2WLIQaNB/E8GViZWj52uvXr1m/fj1Wq5UpU6ao3bRh19fXp1y54+XlxZ07dygsLGTWrFk4OTkxduxYLBYLtbW1TJs2jZ6eHvR6PR4eHjg6OpKUlISzs7PmdhIK8b/QfGliyOnTp9m3bx/BwcEsXbpU0xMrJpMJnU6Ho6Mj9fX1jB49Gjc3NwwGAxEREXR3d3PixAksFgt9fX2kpaUp15oLIf57EsT/MjAwwLlz50hMTNTkxNLXHj16RHJyMgaDgT179tDf38/Vq1cpLy+nvb2djIwMZsyYwdOnT4mLi1O7uUKMeBLE4ofy8vKwWq0cPHhQOUfCZrNx4MABHBwcMJlMMiEnxP+JBLH4oc7OTlJSUti6dSuRkZEMDAzw22+/qd0sIX5JMlknfkiv15OcnMzQMnMJYSH+OZreWSf+WlJSEvDvu+ZkJYQQ/wwpTQghhMqkNCGEECqTIBZCCJVJEAshhMokiIUQQmUSxEIIoTIJYqEpFouFSZMmsXLlyj+8t2PHDiZNmkRXV9ff/n0ZGRlUVFT85WcaGxu/uTxUiO9JEAvNcXR0pLW1lfb2duXZhw8faGpqUrFVQsskiIXm2NvbExsby6VLl5RnNTU1REdHK6/LysqIj49n4cKFrF27ltbWVuDLOdVr1qxhwYIFrFu3jrdv3yo/8+zZM9auXUtCQgIGg4Hz588PX6fEiCZBLDRp0aJFVFZWKq8vXrzI4sWLgS938hUXF1NSUkJVVRXx8fFkZmby+fNn9u7dy7Rp07hy5Qo5OTlKQNtsNkwmE1u2bKGiooLS0lKOHz9Oc3OzKv0TI4tscRaaFBgYiL29PWazGb1eT29vLwEBAQDcvHmTuLg45XLYhIQEcnNzsVgsNDQ0sH37dgB8fHwICwsD4MWLF7x8+ZKdO3cq3/Hx40cePHiAn5/fMPdOjDQSxEKzFi5cSFVVFe7u7hgMBuX5j87U+Pz5MzabjVGjRn1zDdTo0V/+hD59+oSrq+s3o+x3797h6uoqo2LxH0lpQmiWwWDg2rVrVFdXf7OqITw8nOrqamX1RHl5OW5ubvj4+BAeHk5ZWRkAHR0dNDY2AuDr64uTk5MSxK9evSI+Ph6z2TzMvRIjkYyIhWZ5eXnh5+eHq6srbm5uyvOwsDBSU1NJSUlhcHAQd3d3jh49ip2dHbt37yY7O5vY2FjGjx/P5MmTgS/HhBYVFZGbm0txcTE2m42srCxmzpyphLUQf0ZOXxNCCJVJaUIIIVQmQSyEECqTIBZCCJVJEAshhMokiIUQQmUSxEIIoTIJYiGEUJkEsRBCqOx3vCeDnUHc0k4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.catplot(data=acc_df.reset_index(), x='index', y='Accuracy', s=15)\n",
    "plt.xlabel('Model')\n",
    "g.set_xticklabels(rotation=30)\n",
    "plt.ylim([960e-3, 992e-3])\n",
    "plt.title('Accuracy of various models')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The voting classifier wins!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### # 2 Ensemble method: bagging classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up the classifier that will use bootstrap samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_clf = BaggingClassifier(forest_clf, n_estimators=20, max_features=80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                        class_weight=None,\n",
       "                                                        criterion='gini',\n",
       "                                                        max_depth=22,\n",
       "                                                        max_features=80,\n",
       "                                                        max_leaf_nodes=None,\n",
       "                                                        min_impurity_decrease=0.0,\n",
       "                                                        min_impurity_split=None,\n",
       "                                                        min_samples_leaf=2,\n",
       "                                                        min_samples_split=2,\n",
       "                                                        min_weight_fraction_leaf=0.0,\n",
       "                                                        n_estimators=200,\n",
       "                                                        n_jobs=-1,\n",
       "                                                        oob_score=True,\n",
       "                                                        random_state=None,\n",
       "                                                        verbose=0,\n",
       "                                                        warm_start=False),\n",
       "                  bootstrap=True, bootstrap_features=False, max_features=80,\n",
       "                  max_samples=1.0, n_estimators=20, n_jobs=None,\n",
       "                  oob_score=False, random_state=None, verbose=0,\n",
       "                  warm_start=False)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With the bagging classifier, we have reached a 98.312500% accuracy\n"
     ]
    }
   ],
   "source": [
    "bag_accuracy = accuracy_score(y_test, y_pred)\n",
    "print('With the bagging classifier, we have reached a %f%% accuracy'%(bag_accuracy*1_00))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.982500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.986250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extra Trees</th>\n",
       "      <td>0.976875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voting</th>\n",
       "      <td>0.988125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging</th>\n",
       "      <td>0.983125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Accuracy\n",
       "Decision Tree  0.982500\n",
       "Random Forest  0.986250\n",
       "Extra Trees    0.976875\n",
       "Voting         0.988125\n",
       "Bagging        0.983125"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_df = pd.concat([acc_df, pd.DataFrame([bag_accuracy], columns=['Accuracy'], index=['Bagging'])], axis=0)\n",
    "acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Accuracy of various models')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.catplot(data=acc_df.reset_index(), x='index', y='Accuracy', s=15)\n",
    "plt.xlabel('Model')\n",
    "g.set_xticklabels(rotation=30)\n",
    "plt.ylim([960e-3, 992e-3])\n",
    "plt.title('Accuracy of various models')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### # 3 Ensemble method: pasting classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A pasting classifier is like bagging, but instead of sampling training instances with replacement, it does no replacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "paste_clf = BaggingClassifier(forest_clf, n_estimators=20, max_features=80,\n",
    "                           bootstrap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                        class_weight=None,\n",
       "                                                        criterion='gini',\n",
       "                                                        max_depth=22,\n",
       "                                                        max_features=80,\n",
       "                                                        max_leaf_nodes=None,\n",
       "                                                        min_impurity_decrease=0.0,\n",
       "                                                        min_impurity_split=None,\n",
       "                                                        min_samples_leaf=2,\n",
       "                                                        min_samples_split=2,\n",
       "                                                        min_weight_fraction_leaf=0.0,\n",
       "                                                        n_estimators=200,\n",
       "                                                        n_jobs=-1,\n",
       "                                                        oob_score=True,\n",
       "                                                        random_state=None,\n",
       "                                                        verbose=0,\n",
       "                                                        warm_start=False),\n",
       "                  bootstrap=False, bootstrap_features=False, max_features=80,\n",
       "                  max_samples=1.0, n_estimators=20, n_jobs=None,\n",
       "                  oob_score=False, random_state=None, verbose=0,\n",
       "                  warm_start=False)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paste_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = paste_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With the pasting classifier, we have reached a 98.750000% accuracy\n"
     ]
    }
   ],
   "source": [
    "paste_accuracy = accuracy_score(y_test, y_pred)\n",
    "print('With the pasting classifier, we have reached a %f%% accuracy'%(paste_accuracy*1_00))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.982500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.986250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extra Trees</th>\n",
       "      <td>0.976875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voting</th>\n",
       "      <td>0.988125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging</th>\n",
       "      <td>0.983125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pasting</th>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Accuracy\n",
       "Decision Tree  0.982500\n",
       "Random Forest  0.986250\n",
       "Extra Trees    0.976875\n",
       "Voting         0.988125\n",
       "Bagging        0.983125\n",
       "Pasting        0.987500"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_df = pd.concat([acc_df, pd.DataFrame([paste_accuracy], columns=['Accuracy'], index=['Pasting'])], axis=0)\n",
    "acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Accuracy of various models')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.catplot(data=acc_df.reset_index(), x='index', y='Accuracy', s=15)\n",
    "plt.xlabel('Model')\n",
    "g.set_xticklabels(rotation=30)\n",
    "plt.ylim([970e-3, 992e-3])\n",
    "plt.title('Accuracy of various models')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order of accuracy: <br>\n",
    "1. Voting Classifier <br>\n",
    "2. Pasting Classifier <br>\n",
    "3. Random Forest <br>\n",
    "4. Bagging Classifier <br>\n",
    "5. Decision Tree Classifier <br>\n",
    "6. Extra Tree Classifier <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorted Methods by Accuracy Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Voting</th>\n",
       "      <td>0.988125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pasting</th>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.986250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging</th>\n",
       "      <td>0.983125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.982500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extra Trees</th>\n",
       "      <td>0.976875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Accuracy\n",
       "Voting         0.988125\n",
       "Pasting        0.987500\n",
       "Random Forest  0.986250\n",
       "Bagging        0.983125\n",
       "Decision Tree  0.982500\n",
       "Extra Trees    0.976875"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_df.sort_values(by='Accuracy', ascending=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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